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[ "@youchangkim I tried to replicate the provided code, could you have a look at the log below and confirm the same?\r\n```\r\n\r\ntf.Tensor(\r\n[[1.234 2.346 3.457]\r\n [4.566 5.68 6.79 ]], shape=(2, 3), dtype=float16)\r\ntf.Tensor(\r\n[[1.25 2.25 3.5]\r\n [4.5 5.5 7]], shape=(2, 3), dtype=float8_e4m3fn)\r\n---------------------------------------------------------------------------\r\nNotFoundError Traceback (most recent call last)\r\n<ipython-input-1-4393d1d31d50> in <cell line: 10>()\r\n 8 print(a_fp8)\r\n 9 \r\n---> 10 b = a_fp8[1:2] # tensorflow.python.framework.errors_impl.NotFoundError\r\n 11 b = tf.transpose(a_fp8, [1, 0]) # tensorflow.python.framework.errors_impl.NotFoundError\r\n\r\n1 frames\r\n/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py in raise_from_not_ok_status(e, name)\r\n 7260 def raise_from_not_ok_status(e, name):\r\n 7261 e.message += (\" name: \" + name if name is not None else \"\")\r\n-> 7262 raise core._status_to_exception(e) from None # pylint: disable=protected-access\r\n 7263 \r\n 7264 \r\n\r\nNotFoundError: Could not find device for node: {{node StridedSlice}} = StridedSlice[Index=DT_INT32, T=DT_FLOAT8_E4M3FN, begin_mask=0, ellipsis_mask=0, end_mask=0, new_axis_mask=0, shrink_axis_mask=0]\r\nAll kernels registered for op StridedSlice:\r\n device='XLA_GPU_JIT'; Index in [DT_INT32, DT_INT16, DT_INT64]; T in [DT_FLOAT, DT_DOUBLE, DT_INT32, DT_UINT8, DT_INT16, 930109355527764061, DT_HALF, DT_UINT32, DT_UINT64, DT_FLOAT8_E5M2, DT_FLOAT8_E4M3FN]\r\n device='XLA_CPU_JIT'; Index in [DT_INT32, DT_INT16, DT_INT64]; T in [DT_FLOAT, DT_DOUBLE, DT_INT32, DT_UINT8, DT_INT16, 930109355527764061, DT_HALF, DT_UINT32, DT_UINT64, DT_FLOAT8_E5M2, DT_FLOAT8_E4M3FN]\r\n device='CPU'; T in [DT_QINT32]\r\n device='CPU'; T in [DT_QUINT8]\r\n device='CPU'; T in [DT_QINT8]\r\n device='CPU'; T in [DT_VARIANT]\r\n device='CPU'; T in [DT_RESOURCE]\r\n device='CPU'; T in [DT_STRING]\r\n device='CPU'; T in [DT_BOOL]\r\n device='CPU'; T in [DT_COMPLEX128]\r\n device='CPU'; T in [DT_COMPLEX64]\r\n device='CPU'; T in [DT_DOUBLE]\r\n device='CPU'; T in [DT_FLOAT]\r\n device='CPU'; T in [DT_BFLOAT16]\r\n device='CPU'; T in [DT_HALF]\r\n device='CPU'; T in [DT_INT32]\r\n device='CPU'; T in [DT_INT8]\r\n device='CPU'; T in [DT_UINT8]\r\n device='CPU'; T in [DT_INT16]\r\n device='CPU'; T in [DT_UINT16]\r\n device='CPU'; T in [DT_UINT32]\r\n device='CPU'; T in [DT_INT64]\r\n device='CPU'; T in [DT_UINT64]\r\n device='GPU'; T in [DT_INT32]\r\n device='GPU'; T in [DT_BOOL]\r\n device='GPU'; T in [DT_COMPLEX128]\r\n device='GPU'; T in [DT_COMPLEX64]\r\n device='GPU'; T in [DT_DOUBLE]\r\n device='GPU'; T in [DT_FLOAT]\r\n device='GPU'; T in [DT_BFLOAT16]\r\n device='GPU'; T in [DT_HALF]\r\n device='GPU'; T in [DT_INT8]\r\n device='GPU'; T in [DT_UINT8]\r\n device='GPU'; T in [DT_INT16]\r\n device='GPU'; T in [DT_UINT16]\r\n device='GPU'; T in [DT_UINT32]\r\n device='GPU'; T in [DT_INT64]\r\n device='GPU'; T in [DT_UINT64]\r\n [Op:StridedSlice] name: strided_slice/\r\n\r\n```\r\nThank you!", "@sushreebarsa Yes. The ask is to register float8 types to these data movement ops.", "@sachinprasadhs I was able to replicate the issue reported here using TF [2.12](https://colab.research.google.com/gist/sushreebarsa/74637819d391171f4f910485036fb301/61374.ipynb) and [2.13 ](https://colab.research.google.com/gist/sushreebarsa/2ce5f5ab09db398b0456196555f760e4/61374.ipynb)as well. Please have a look ?\r\nThank you! ", "Fixed with https://github.com/tensorflow/tensorflow/pull/61749" ]

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[ "Hi @njuhang \r\n\r\nCan you please pull from master branch and let us know if you are facing same issue?\r\n\r\nWork groups from the below are currently used in TFLite GPU and their stability is statistically proven. While\r\nthey do not necessarily result in peak optimal time across all parameters, they are reliable in giving top 10% performance\r\nregardless of the convolution parameters.\r\n\r\n\r\n\r\nAdreno GPU Model | CONV 2D | DEPTHWISE CONV\r\n-- | -- | --\r\n630 | (4, 8, 4) | (4, 4, 8)\r\n540 | (8, 2, 2) | (8, 8, 2)\r\n510 | (8, 4, 4) | (8, 4, 4)\r\n509 | (8, 4, 8) | (8, 4, 2)\r\n50X/4XX | (8, 4, 8) | (8, 4, 8)\r\n\r\nPlease check this paper for reference: https://arxiv.org/abs/1907.01989\r\n\r\nThanks.", "Thanks for reply!\r\nI download benchmark file from https://storage.googleapis.com/tensorflow-nightly-public/prod/tensorflow/release/lite/tools/nightly/latest/android_aarch64_benchmark_model_plus_flex, then benchmark on my sm6225 device and got the same error. \r\nCommand is ./android_aarch64_benchmark_model --graph=./models/mobilenet_v2_float.tflite --use_gpu=true", "@njuhang Thanks for the information.\r\n\r\nAs this [comment](https://github.com/tensorflow/tensorflow/issues/34064#issuecomment-551936622) suggests,\r\n\r\nThe max Local Work group size of each device is often 256. We may have to experiment with the workgroup sizes and it is observed that Adreno is more susceptible to workgroup changes. Change in group size will be affecting in the performance.\r\n\r\nThanks.", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "I see it. But I think it will result some errors on some devices such as sm6225, and it need to be solved.", "@njuhang Thanks for the information.\r\n\r\n@pkgoogle Could you please look into this issue?\r\n\r\nThanks.", "Hi @njuhang, thanks for reporting the issue. Can you help me with more context. Are you using the flex version of android_aarch64_benchmark_model? Because your command doesn't have it but your download link does. Can you upload the .tflite model so we can replicate? Are you doing this in android studio? Can you show me the code around where you invoke the model? A toy project with just the offending code will be preferable so that we can reproduce it locally.", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61373\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61373\">No</a>\n" ]

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[ "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61372\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61372\">No</a>\n" ]

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[ "Hi @WenjieZ ,\r\n\r\nOfficially we don't recommend users to install tensorflow with conda-forge.We officially recommend to use only Pip since TensorFlow is only officially released to PyPI.\r\n\r\nPlease refer to attached [source](https://www.tensorflow.org/install/pip#:~:text=Note%3A%20Do%20not%20install%20TensorFlow%20with%20conda.%20It%20may%20not%20have%20the%20latest%20stable%20version.%20pip%20is%20recommended%20since%20TensorFlow%20is%20only%20officially%20released%20to%20PyPI.) for same.Thanks!", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "Thanks for the reply. I thought that the conda-forge version was released by Google as well. ", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61371\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61371\">No</a>\n" ]

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[ "@sronen71 For LSTM conversion, this is an [article](https://www.tensorflow.org/lite/convert/rnn) and colab describing the process, and you can follow the [colab gist](https://colab.sandbox.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/lite/examples/experimental_new_converter/Keras_LSTM_fusion_Codelab.ipynb) to do it. For [4 kinds of quantizations](https://www.tensorflow.org/lite/performance/model_optimization), maybe you can also try other ways like dynamic range. Hope it could help? Please let us know. Thank you!", "@sushreebarsa \r\nI checked that information and colab example previously, and based my colab gist on that.\r\nI find the original colab example is broken right now, because of tf-nightly error.\r\nI was able to run it yesterday successfully, however, it only uses the vanilla convert.\r\nWhen I try to use the optimize option with float16 target, as in the colab gist I attached, the reported defect is observed.", "@pkgoogle I was able to reproduce this issue. Please find this [gist](https://colab.research.google.com/gist/pjpratik/f814b84dc79edfee54977c82c98cc34c/61370.ipynb).\r\n\r\nCould you please look into this?\r\n\r\nThanks.", "I was able to replicate with @pjpratik's exact same gist, @abattery, can you please take a look? Thanks.", "Hi, can i work on this issue ?", "Hi @devadigapratham, we always welcome any contribution from the community. Please let us know if you have any questions.", "Also running into this issue for float16 tflite conversions" ]

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[ "Hi @brunolucatto,\r\n\r\nPickling isn't natively supported in keras as it is generally [not secure](https://docs.python.org/3/library/pickle.html). Therefore I don't think this is actionable on Keras side, and overriding methods for `tf.keras.layers.serialize` is necessary. \r\n\r\nPlease refer to the similar issue [here](https://github.com/keras-team/tf-keras/issues/626).\r\n\r\nPlease look at the [gist](https://colab.research.google.com/gist/Varsha-anjanappa/f87d4145f9d2a2a9e46069790154b4f6/61369.ipynb) here.\r\n\r\nPlease let us know if it works.\r\n\r\nThank you!!\r\n", "Hi @Varsha-anjanappa, thank you so much for the prompt reply.\r\n\r\nI am afraid your example on gist missed the part where it fails, which is in the deserialization, not in the serialization. I added the tf.keras.layers.deserialize function, and it throws the same error as the pickle one.\r\n\r\n```\r\nimport tensorflow as tf\r\nimport pickle\r\n\r\nmodel_input = tf.keras.Input(shape=(1,), dtype=tf.int64)\r\nlookup = tf.keras.layers.StringLookup(vocabulary=['a', 'b'])(model_input)\r\nlookup = tf.keras.layers.Flatten()(lookup)\r\noutput = tf.keras.layers.Dense(10)(lookup)\r\nfull_model = tf.keras.Model(model_input, output)\r\n\r\n# this part should work\r\nmodel_bytes = pickle.dumps(full_model)\r\nmodel_recovered = pickle.loads(model_bytes)\r\n\r\n\r\n# this part should throw an error\r\nfull_model.save(\"/tmp/temp_model\")\r\nfull_model_loaded = tf.keras.models.load_model(\"/tmp/temp_model\")\r\nmodel_bytes_2 = tf.keras.layers.serialize(full_model_loaded)\r\nmodel_recovered_2 = tf.keras.layers.deserialize(model_bytes_2)\r\n```\r\n\r\nMoreover, I also added the proposed solution for the problem (i.e., changing the get_config method of the StringLookup class) to show that it also works using Keras' proper serialization.\r\n\r\n```\r\nimport tensorflow as tf\r\nimport pickle\r\n\r\[email protected]_keras_serializable()\r\nclass MyStringLookup(tf.keras.layers.StringLookup):\r\n def get_config(self):\r\n base_config = super().get_config()\r\n custom = {\"vocabulary\": self.get_vocabulary()}\r\n return {**base_config, **custom}\r\n\r\nmodel_input = tf.keras.Input(shape=(1,), dtype=tf.int64)\r\nlookup = MyStringLookup(vocabulary=['a', 'b'])(model_input)\r\nlookup = tf.keras.layers.Flatten()(lookup)\r\noutput = tf.keras.layers.Dense(10)(lookup)\r\nfull_model = tf.keras.Model(model_input, output)\r\n\r\n# this part should work\r\nmodel_bytes = pickle.dumps(full_model)\r\nmodel_recovered = pickle.loads(model_bytes)\r\n\r\n\r\n# this part should throw an error\r\nfull_model.save(\"/tmp/temp_model\")\r\nfull_model_loaded = tf.keras.models.load_model(\"/tmp/temp_model\")\r\nmodel_bytes_2 = tf.keras.layers.serialize(full_model_loaded)\r\nmodel_recovered_2 = tf.keras.layers.deserialize(model_bytes_2)\r\n```\r\n\r\n#-----------------\r\n\r\nAs a side note, I am using pickle because I want to distribute the model on spark using broadcast, so I can make predictions using a pandas UDF more efficiently. The implementation of pyspark.broadcast itself uses pickle, I tried to change that by creating a custom broadcast function but it was not an easy task for me. Loading the model from the disk inside the pandas UDF is also an alternative to broadcast, but it is many times slower.\r\n\r\n", "Hi @Varsha-anjanappa,\r\n\r\nJust following up to know if you were able to reproduce the error with the changes above.\r\n\r\nPlease let me know if I can provide any further information.", "Hi @brunolucatto ,\r\n\r\nPlease find the attached [gist](https://colab.research.google.com/gist/Varsha-anjanappa/89c9def29103fc1b3aa3735f427362a7/61369_v2.ipynb).\r\n\r\nAs you can see in the gist, when we serialize, the vocabulary values aren't captured, it gives an empty list. Whereas the vocab size is getting captured. There is some issue in the source code. We'll check and get back to you.\r\n\r\nThank you ", "@SuryanarayanaY , can you please look into it", "we are seeing the same issue using tf 2.6.2 with python 3.6.9", "@brunolucatto ,\r\n\r\nIt seems a valid issue for me. It needs to dig more for the root cause.", "I have done some work, on this bug. The issue seems to be in this file load.py. \r\n\r\n\r\nAs I have highlighted, parse from string is generating the data as nodes via proto buff.\r\n\r\n\r\n\r\nThe output nodes are as follows, \r\n\r\nnodes {\r\n node_id: 1\r\n node_path: \"root.layer-0\"\r\n identifier: \"_tf_keras_input_layer\"\r\n metadata: \"{\\\"class_name\\\": \\\"InputLayer\\\", \\\"name\\\": \\\"input_1\\\", \\\"dtype\\\": \\\"int64\\\", \\\"sparse\\\": false, \\\"ragged\\\": false, \\\"batch_input_shape\\\": {\\\"class_name\\\": \\\"__tuple__\\\", \\\"items\\\": [null, 1]}, \\\"config\\\": {\\\"batch_input_shape\\\": {\\\"class_name\\\": \\\"__tuple__\\\", \\\"items\\\": [null, 1]}, \\\"dtype\\\": \\\"int64\\\", \\\"sparse\\\": false, \\\"ragged\\\": false, \\\"name\\\": \\\"input_1\\\"}}\"\r\n version {\r\n producer: 2\r\n min_consumer: 1\r\n }\r\n}\r\nnodes {\r\n node_id: 2\r\n node_path: \"root.layer-1\"\r\n identifier: \"_tf_keras_layer\"\r\n metadata: \"{\\\"name\\\": \\\"string_lookup\\\", \\\"trainable\\\": true, \\\"expects_training_arg\\\": false, \\\"dtype\\\": \\\"int64\\\", \\\"batch_input_shape\\\": null, \\\"stateful\\\": false, \\\"must_restore_from_config\\\": true, \\\"preserve_input_structure_in_config\\\": false, \\\"autocast\\\": true, \\\"class_name\\\": \\\"StringLookup\\\", \\\"config\\\": {\\\"name\\\": \\\"string_lookup\\\", \\\"trainable\\\": true, \\\"dtype\\\": \\\"int64\\\", \\\"invert\\\": false, \\\"max_tokens\\\": null, \\\"num_oov_indices\\\": 1, \\\"oov_token\\\": \\\"[UNK]\\\", \\\"mask_token\\\": null, \\\"output_mode\\\": \\\"int\\\", \\\"sparse\\\": false, \\\"pad_to_max_tokens\\\": false, \\\"idf_weights\\\": null, \\\"vocabulary\\\": null, \\\"vocabulary_size\\\": 3, \\\"encoding\\\": \\\"utf-8\\\", \\\"has_input_vocabulary\\\": true}, \\\"inbound_nodes\\\": [[[\\\"input_1\\\", 0, 0, {}]]], \\\"shared_object_id\\\": 1, \\\"build_input_shape\\\": {\\\"class_name\\\": \\\"TensorShape\\\", \\\"items\\\": [null, 1]}}\"\r\n version {\r\n producer: 2\r\n min_consumer: 1\r\n }\r\n}\r\nnodes {\r\n node_id: 3\r\n node_path: \"root.layer_with_weights-0\"\r\n identifier: \"_tf_keras_layer\"\r\n metadata: \"{\\\"name\\\": \\\"dense\\\", \\\"trainable\\\": true, \\\"expects_training_arg\\\": false, \\\"dtype\\\": \\\"float32\\\", \\\"batch_input_shape\\\": null, \\\"stateful\\\": false, \\\"must_restore_from_config\\\": false, \\\"preserve_input_structure_in_config\\\": false, \\\"autocast\\\": true, \\\"class_name\\\": \\\"Dense\\\", \\\"config\\\": {\\\"name\\\": \\\"dense\\\", \\\"trainable\\\": true, \\\"dtype\\\": \\\"float32\\\", \\\"units\\\": 10, \\\"activation\\\": \\\"linear\\\", \\\"use_bias\\\": true, \\\"kernel_initializer\\\": {\\\"class_name\\\": \\\"GlorotUniform\\\", \\\"config\\\": {\\\"seed\\\": null}, \\\"shared_object_id\\\": 2}, \\\"bias_initializer\\\": {\\\"class_name\\\": \\\"Zeros\\\", \\\"config\\\": {}, \\\"shared_object_id\\\": 3}, \\\"kernel_regularizer\\\": null, \\\"bias_regularizer\\\": null, \\\"activity_regularizer\\\": null, \\\"kernel_constraint\\\": null, \\\"bias_constraint\\\": null}, \\\"inbound_nodes\\\": [[[\\\"string_lookup\\\", 0, 0, {}]]], \\\"shared_object_id\\\": 4, \\\"input_spec\\\": {\\\"class_name\\\": \\\"InputSpec\\\", \\\"config\\\": {\\\"dtype\\\": null, \\\"shape\\\": null, \\\"ndim\\\": null, \\\"max_ndim\\\": null, \\\"min_ndim\\\": 2, \\\"axes\\\": {\\\"-1\\\": 1}}, \\\"shared_object_id\\\": 7}, \\\"build_input_shape\\\": {\\\"class_name\\\": \\\"TensorShape\\\", \\\"items\\\": [null, 1]}}\"\r\n version {\r\n producer: 2\r\n min_consumer: 1\r\n }\r\n\r\nin node_id:2 the vocabulary is getting dropped where as it is present in file content. Issue seems to be with protobuff and parsing. It's 2 Am and I have worked on this for straight 4 hours and am taking a break. If you can wait will resume working on it 🙂.\r\n\r\n\r\n", "My bad, I think that there is some mistake in how model is saving metadata for the layers.\r\n\r\nOpened the model and looked at metadata:\r\n\r\n\r\n", "Hi folks, has there been any work on this by any chance or is there a workaround for this? I am currently running into this issue as well. ", "@sharyar Isn't the workaround proposed in the original post working in your case?\r\n\r\n> We were able to circumvent the issue by creating a new class as follows:\r\n> \r\n> ```\r\n> @tf.keras.utils.register_keras_serializable()\r\n> class MyStringLookup(tf.keras.layers.StringLookup):\r\n> def get_config(self):\r\n> base_config = super().get_config()\r\n> custom = {\"vocabulary\": self.get_vocabulary()}\r\n> return {**base_config, **custom}\r\n> ```", "> We were able to circumvent the issue by creating a new class as follows:\r\n> \r\n> ```\r\n> @tf.keras.utils.register_keras_serializable()\r\n> class MyStringLookup(tf.keras.layers.StringLookup):\r\n> def get_config(self):\r\n> base_config = super().get_config()\r\n> custom = {\"vocabulary\": self.get_vocabulary()}\r\n> return {**base_config, **custom}\r\n> ```\r\n> \r\n> However, it would be nice if we didn't have to create this wrapper.\r\n\r\nHi @brunolucatto ,\r\nThe decorator tf.keras.utils.register_keras_serializable() is one of the way for serializing a class/method into Keras object which can then be saved as .keras model and the able to reload successfully.\r\n\r\nThere are 3 ways we can do this as mentioned here in [custom_objects](https://www.tensorflow.org/guide/keras/serialization_and_saving#custom_objects) serialization_and_saving guide . You can choose any one.\r\n\r\n\r\n", "Hi @SuryanarayanaY, thanks for the reply!\r\n\r\nI think the main issue is not the decorator, but the fact that we need to create this class to make the serialization work. The only thing this new class is doing is \"saving again\" the vocabulary attribute of the instance, and all of this would be unnecessary if the StringLookup layer serialization was working properly. (*)\r\n\r\nCreating this class is a somewhat ugly workaround for the problem, as it does not address the root cause directly. This is the reason I did not submit it as a potential fix, although it has been working nicely so far.\r\n\r\n(*) _In the original post, I referred to this new class as a wrapper, which in hindsight makes it look like it was only the decorator that was making me unhappy. Sorry for the ambiguity there._", "I ran into this issue recently when passing a keras model object to sklearn [permutation_importance()](https://scikit-learn.org/stable/modules/generated/sklearn.inspection.permutation_importance.html) and was hitting this error when using `n_jobs` in that function. \r\n\r\nIn my case i had a model object that has `StringLookup` layers i need to replace with the `MyStringLookup` layers. Using [`clone_model`](https://www.tensorflow.org/api_docs/python/tf/keras/models/clone_model) helped like this: \r\n\r\n```\r\nimport tensorflow as tf\r\nimport pickle\r\n\r\ntf.keras.utils.get_custom_objects().clear()\r\n\r\n\r\[email protected]_keras_serializable()\r\nclass MyStringLookup(tf.keras.layers.StringLookup):\r\n def get_config(self):\r\n base_config = super().get_config()\r\n vocabulary = self.get_vocabulary()\r\n custom = {\"vocabulary\": vocabulary}\r\n return {**base_config, **custom}\r\n\r\n\r\ndef my_clone_function(layer):\r\n if isinstance(layer, tf.keras.layers.StringLookup):\r\n clone_layer = MyStringLookup(vocabulary=layer.get_vocabulary())\r\n return clone_layer\r\n return layer\r\n\r\n\r\n# create model\r\nmodel_input = tf.keras.Input(shape=(1,), dtype=tf.int64)\r\nlookup = tf.keras.layers.StringLookup(vocabulary=['a', 'b'])(model_input)\r\noutput = tf.keras.layers.Dense(10)(lookup)\r\nmodel = tf.keras.Model(model_input, output)\r\n\r\n# model StringLookup has vocabulary set\r\nprint('\\ncorrect:\\n')\r\nprint(model.layers[1].get_config()['vocabulary'])\r\n# ListWrapper(['a', 'b'])\r\n\r\n# save model\r\nmodel.save(\"/tmp/temp_model\")\r\n\r\n# load model\r\nmodel_orginal_reloaded = tf.keras.models.load_model(\"/tmp/temp_model\")\r\n\r\n# model StringLookup now has no vocabulary set\r\nprint('\\nbroken:\\n')\r\nprint(model_orginal_reloaded.layers[1].get_config()['vocabulary'])\r\n# ListWrapper([])\r\n\r\n# fix the model\r\nmodel_fixed = tf.keras.models.clone_model(\r\n model,\r\n clone_function=my_clone_function\r\n)\r\n\r\n# we now see a correct model vocab in the MyStringLookup layer\r\nprint('\\nfixed:\\n')\r\nprint(model_fixed.layers[1].get_config()['vocabulary'])\r\n```\r\n\r\n[Here is a google colab](https://colab.research.google.com/drive/1qmdgR7a06oHQgFs1_jiOWggQJJJyYrqV?usp=sharing) to show what i mean.\r\n\r\nJust sharing this in case is useful for anyone else in a similar situation." ]

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[ "Thanks for your pull request! It looks like this may be your first contribution to a Google open source project. Before we can look at your pull request, you'll need to sign a Contributor License Agreement (CLA).\n\nView this [failed invocation](https://github.com/tensorflow/tensorflow/pull/61368/checks?check_run_id=15299055578) of the CLA check for more information.\n\nFor the most up to date status, view the checks section at the bottom of the pull request.", "Hi @sabauma Can you please resolve conflicts? Thank you!", "I think I've resolved all the existing issues. Just waiting for @rsuderman to give another pass.", "The changes that were originally in this PR now appear in the upstream repo on the master branch. I'm not sure what is going on, as this PR was never accepted. This PR now appears to be mostly empty due to all the changes being moved upstream.\r\n\r\n@gbaned would you have some insight into what is going on?", "Hi @rsuderman Can you please assist on above comments from @sabauma. Thank you!", "Hi @rsuderman Any update on this PR? Please. Thank you!", "Hi @rsuderman Any update on this PR? Please. Thank you!", "Hi @rsuderman Any update on this PR? Please. Thank you!", "Hi @rsuderman Any update on this PR? Please. Thank you!", "Hi @rdzhabarov Can you please assist on above [comments](https://github.com/tensorflow/tensorflow/pull/61368#issuecomment-1669593469) from @sabauma. Thank you!\r\n\r\n" ]

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[ "Hi @Seferovic8 ,\r\n\r\nThe issue resolved in TF2.13v. Please refer to attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/dbdb3fc33d50e28dc9dc5093ddc7ae77/61366.ipynb) and may test yourself. Since it is resolved in latest version its unlikely to cherry pick for TF2.12 version.", "Thank you, this works for me.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61366\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61366\">No</a>\n", "Yes\r\n\r\nOn Tue, 25 Jul 2023 at 16:41, google-ml-butler[bot] <\r\n***@***.***> wrote:\r\n\r\n> Are you satisfied with the resolution of your issue?\r\n> Yes\r\n> <https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61366>\r\n> No\r\n> <https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61366>\r\n>\r\n> —\r\n> Reply to this email directly, view it on GitHub\r\n> <https://github.com/tensorflow/tensorflow/issues/61366#issuecomment-1649975456>,\r\n> or unsubscribe\r\n> <https://github.com/notifications/unsubscribe-auth/AP67UQMQM5KVXEQMSSPJVO3XR7LH3ANCNFSM6AAAAAA2V3OBRU>\r\n> .\r\n> You are receiving this because you modified the open/close state.Message\r\n> ID: ***@***.***>\r\n>\r\n" ]

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[ "@nSircombe @milpuz01 @cfRod this should not impact the ARM build as it will continue to be built with ver2 macro set. Please test and let us know.", "@penpornk Thank you for reviewing this PR. I have addressed your review comments. Please take a look.", "@penpornk Thanks for approving the PR. Please note that we also need to add onednn repo in TSL's workspace [file](https://github.com/google/tsl/blob/main/workspace2.bzl#L157). Would you be able to add it? Thanks.", "@bhavani-subramanian Will do. Thank you for the heads up!\r\n\r\nQuick notes on CIs for future reference:\r\n\r\n[ROCm](http://ml-ci.amd.com:21096/blue/organizations/jenkins/tensorflow%2Fgithub-prs-upstream-master%2FAMD-ROCm-Community-CI-Build/detail/PR-61365/7/pipeline) errors are unrelated. I've seen them on other PRs:\r\n```\r\nERROR: /workspace/tensorflow/compiler/xla/service/gpu/BUILD:853:11: in deps attribute of cc_library rule //tensorflow/compiler/xla/service/gpu:gpu_executable: Label '//tensorflow/tsl/platform:random' is duplicated\r\n\r\nERROR: /workspace/tensorflow/compiler/xla/service/gpu/BUILD:853:11: Analysis of target '//tensorflow/compiler/xla/service/gpu:gpu_executable' failed\r\n```\r\n[MacOS](https://source.cloud.google.com/results/invocations/5a867fb3-7b77-4530-9041-1ed3a8403069/log) failure is unrelated. (Test timed out)\r\n```\r\n[38,742 / 38,767] 771 / 1022 tests, 1 failed; Testing //tensorflow/python/data/kernel_tests:interleave_test (shard 1 of 24); 18s local, remote-cache ... (16 actions running)\r\n[38,742 / 38,767] 771 / 1022 tests, 1 failed; Testing //tensorflow/python/data/kernel_tests:interleave_test (shard 1 of 24); 27s local, remote-cache ... (16 actions running)\r\n\r\n\r\nERROR: Aborting VM command due to timeout of 43200 seconds\r\n```\r\n\r\n[Py+CPP Ubuntu CPU](https://source.cloud.google.com/results/invocations/3df54b96-e627-4995-9afa-0ef383d7f6ab/log) failure is unrelated. All 6 failed unit tests timed out.\r\n\r\n[Py+CPP Ubuntu GPU](https://source.cloud.google.com/results/invocations/c3b02db3-3730-4e1a-bd80-10e662c6eb89/log) failure is unrelated. The failed test timed out.\r\n```\r\n//tensorflow/python/kernel_tests/linalg:linear_operator_identity_test_gpu TIMEOUT in 2 out of 5 in 466.9s\r\n```\r\n\r\n" ]

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[ "@j99ca Could you please make sure that the source and destination paths are appropriate? As per the doc [here](https://www.tensorflow.org/api_docs/python/tf/io/gfile/rename) , it should raise [errors.OpError](https://www.tensorflow.org/api_docs/python/tf/errors/OpError) if it fails?\r\nThank you!", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "@sushreebarsa I have indeed confirmed the behaviour. It works fine when I revert the tensorflow version number. Can you replicate it in S3?", "Hi, \r\n\r\nCould you please close your old issue here https://github.com/tensorflow/tensorflow/issues/60165 to track the status at single place.\r\n", "@sachinprasadhs I have closed the other story", "@sachinprasadhs is there any resolution to this issue? It is still preventing us from migrating to a newer version of tensorflow above 2.5", "Has anyone found a resolution or workaround for this issue?" ]

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[ "@nhuet I tried to replicate the issue on colab using TF [v2.12](https://colab.research.google.com/gist/sushreebarsa/e498716b10932a7d557ab3a3a9fccb76/61363-2-12.ipynb) and [2.13](https://colab.research.google.com/gist/sushreebarsa/3ec5e639c34bb5c3d965c531fe1090b3/untitled813.ipynb#scrollTo=UpjWS8k2G3ms), could you please have a look at the attached gists and confirm the same?\r\nThank you!", "It seems to show the issue (even though only 3* slower, this is still abnormal)", "@nhuet thank you for the quick response\r\n@sachinprasadhs Could you please have a look at this. Thank you!", "Hi @nhuet, thank you for bringing up this issue! Would you be able to try to replicate this with the `keras_core` (https://keras.io/keras_core/) package as this will supersede current Keras. Thank you!", "Hi @grasskin. You are right I should have definitely tried this. And this works very well indeed (even better than keras2.12 + tf 2.12). Here are the new timings:\r\n- keras 2.12.0 + tensorflow 2.12.1: 7.8 s\r\n- keras 2.13.1 + tensorflow 2.13.0: 24.1 s\r\n- keras-core 0.1.3 + tensorflow 2.12.1: 0.4 s\r\n- keras-core 0.1.3 + tensorflow 2.13.0: 0.3 s\r\n\r\n(and even increasing the number of iteration, there is not noticeable differences between keras-core + tf2.12 or keras-core + tf2.13)\r\n\r\nThus, the \"bug\" came from keras 2.13, and the performance is way better with keras-core than with previous keras 2.12.\r\nThanks a lot!", "Hi @nhuet , Thanks for confirming your findings with the keras-core. \r\nI'm glad that keras-core + tensorflow gave the good performance result.\r\n\r\nFor latest package details, please visit https://pypi.org/project/keras-core/\r\n\r\nFeel free to close the issue. Thanks!", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61363\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61363\">No</a>\n" ]

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[ "I have encountered the same issue as you.", "@mars1248,\r\nCould you please confirm the bazel version you are trying to compile and also whether you are building from source or any other way?\r\nLooks like the t**hird_party/gpus/rocm_configure.bzl** file does not include a path for clang 16.0.0. Could you please try manually include it now and please report back whether this allows it to compile successfully.\r\n\r\n Thank you!", "I use tensorflow/build:2.13-python3.11 image, with bazel 5.3.0. and build from source code.\r\nI add this **inc_dirs.append(\"/usr/lib/llvm-16/lib/clang/16/include\")** code to **third_party/gpus/rocm_configure.bzl** but get this error.\r\n`ERROR: /root/.cache/bazel/_bazel_root/645133528a7a8476e9bedcf54eb858b4/external/llvm-project/llvm/BUILD.bazel:184:11: Compiling llvm/lib/Support/Atomic.cpp failed: (Exit 1): clang failed: error executing command /usr/bin/clang -MD -MF bazel-out/k8-opt-exec-50AE0418/bin/external/llvm-project/llvm/_objs/Support/Atomic.d ... (remaining 87 arguments skipped)\r\nIn file included from external/llvm-project/llvm/lib/Support/Atomic.cpp:13:\r\nIn file included from external/llvm-project/llvm/include/llvm/Support/Atomic.h:20:\r\nIn file included from external/llvm-project/llvm/include/llvm/Support/DataTypes.h:19:\r\nIn file included from external/llvm-project/llvm/include/llvm-c/DataTypes.h:43:\r\n/usr/include/x86_64-linux-gnu/sys/types.h:144:10: fatal error: 'stddef.h' file not found\r\n#include <stddef.h>\r\n ^~~~~~~~~~\r\n1 error generated.`", "Use ```/usr/lib/llvm-16/bin/clang``` instead of ```/usr/bin/clang-16```,this may help.\r\n\r\nmy build env(not using Docker):\r\n- Ubuntu 22.04\r\n- Clang 16.0.6 from https://apt.llvm.org/\r\n- Python 3.10\r\n- Bazel 5.3.0\r\n- cuDnn 8.6\r\n- CUDA 11.8\r\nconfigure:\r\n- No ROCm support\r\n- cuda and tensorrt support\r\n" ]

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[ "Hi @Ajim0907 \r\n\r\nWe see that template has not been filled. Could you please fill the issue template ?\r\n\r\nThanks.", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further." ]

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[ "@xiedeacc \r\nWill it be possible for you to upgrade the TF version to the latest?\r\nCould you please have a look at the below configurations and let us know if it helps?\r\n\r\nVersion | Python version | Compiler | Build tools\r\n-- | -- | -- | --\r\ntensorflow-2.13.0 | 3.8-3.11 | Clang 16.0.0 | Bazel 5.3.0\r\n \r\nPlease follow [this](https://www.tensorflow.org/install/source) document for more information on this. \r\nThank you!", "fix by config.set_allow_soft_placement(true)", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61360\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61360\">No</a>\n", "@xiedeacc Glad it worked fine for you. Thank you!" ]

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[ "I noticed that I have `legalize-tf.mlir` tests failing due to the new tests I added. How do I go about fixing them? I'm not really sure what I'm doing differently compared to say the test for `tfl.real` which I based my tests on.\r\n\r\nQuick edit - fixed the test.", "Bump. I've noticed that other people are making changes to similar files. Before I go and resolve the conflicts/regenerate the schema, is there any plan to review this PR in the near term?", "Hi @drubinstein Sorry for the delay, can you please resolve the conflicts? once it done we will process it further. Thank you!", "Thanks @gbaned . I resolved all the conflicts. I have a similar PR, #61280, that has similar conflicts as well that I'm also awaiting review for.", "@gbaned @alankelly , it looks like there are conflicts again and they seem to be happening faster than an assigned reviewer can get to this PR. Are there currently any plans related TFLite and HLO that may be delaying the review of this PR?", "Hi @alankelly Can you please assist on above [comments](https://github.com/tensorflow/tensorflow/pull/61359#issuecomment-1702645376) from @drubinstein. Thank you!", "Hi @zichuan-wei Can you please assist on above https://github.com/tensorflow/tensorflow/pull/61359#issuecomment-1702645376 from @drubinstein. Thank you!", "Hi @zichuan-wei / @alankelly Any update on this PR? Please. Thank you!", "Hi @zichuan-wei / @alankelly Any update on this PR? Please. Thank you!", "Hi @zichuan-wei / @alankelly Any update on this PR? Please. Thank you!", "Hi @drubinstein Can you please resolve conflicts? Thank you!", "Hi @gbaned , I resolved the PR conflicts. Does that mean I should expect a review soon and the tflite kernels are going to be stable for a bit?", "Hi @drubinstein Can you please resolve conflicts? Thank you!", "@gbaned as I've asked before, if I resolve the conflicts, will these files stay stable enough for an actual review? It's been 9 months since I've opened this PR and it feels like I've done conflict resolution a half dozen times with no review (or a hint of review) from the reviewers.", "> @gbaned as I've asked before, if I resolve the conflicts, will these files stay stable enough for an actual review? It's been 9 months since I've opened this PR and it feels like I've done conflict resolution a half dozen times with no review (or a hint of review) from the reviewers.\r\n\r\nHi @drubinstein Sorry for the delay, please resolve the conflicts, we will process the PR for the review. Thank you so much!", "@gbaned , I resolved the conflicts and pushed the updates.", "Hi @Ferev Can you please review this PR? Thank you!", "Hi @drubinstein Can you please resolve conflicts? Thank you!" ]

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[ "@jyingl3 can you have a look? thanks", "> @jyingl3 can you have a look? thanks\r\n\r\nThanks Zhoulong. It looks good to me. Added Penporn as it is adding new TF C APIs.", "@penpornk can you help to have a look? thanks" ]

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[ "I think following changes can solve the TODO:\r\n\r\n[line 351-364](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/reduction_gpu_kernels.cu.h#L351):\r\n```\r\n#if GOOGLE_CUDA || TENSORFLOW_USE_ROCM\r\n __shared__ storage_type<value_type>\r\n partial_sums[TF_RED_WARPSIZE * (TF_RED_WARPSIZE + 1)];\r\n#endif\r\n```\r\n\r\n[line 392](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/reduction_gpu_kernels.cu.h#L392):\r\n`min(blockDim.y, num_rows - blockIdx.y * blockDim.y); // MSVC type casting fix`\r\n\r\nThese changes can be compiled successfully in Windows CUDA environment. Can someone confirm if that's the meaning of the TODO?", "Hi @johnnkp \r\n\r\nProposed PR has been merged now. Could you please check and close this issue.\r\n\r\nThank you!!", "The mentioned PR does not include my proposed changes. I am going to create a PR for this issue.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61357\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61357\">No</a>\n", "Hi @johnnkp \r\n```\r\nmin(blockDim.y, num_rows - blockIdx.y * blockDim.y); // MSVC type casting fix\r\n```\r\nthis caused a lot of build errors due to ```call to 'min' is ambiguous```\r\nhttps://github.com/tensorflow/tensorflow/pull/61638\r\n" ]

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[ "Hi @MrTreev ,\r\n\r\nCould you please test with the below configurations of Clang and Bazel and let us know if problem still persists.Because higher versions may or may not compatible. It seems your Clang version is 17.0 against tested version of 16.0.0. Same for Bazel also where it seems you have 6.1.0 installed and tested version is 5.3.0 for Tf2.13.\r\n\r\n\r\n\r\nVersion | Python version | Compiler | Build tools\r\n-- | -- | -- | --\r\ntensorflow-2.13.0 | 3.8-3.11 | Clang 16.0.0 | Bazel 5.3.0\r\n\r\nYou can find the build instructions [here](https://www.tensorflow.org/install/source#install_python_and_the_tensorflow_package_dependencies).\r\n", "I certainly can try that either later tonight or tomorrow morning (AEST). I'll get back to you when that's done.", "On the r2.13 branch, I've switched to Bazel 5.3.0, I'll try the other changes in the morning tomorrow, but so far, no difference in the error (Been doing full clean builds each time)", "Hi @MrTreev ,\r\n\r\nKindly update on this. Thanks!", "Hi @SuryanarayanaY, I'm attempting to get Clang 16 reliably working at the moment, Sadly the archlinux repos currently have only 15 and 17, so I'm having to do it manually and trying not to break the rest of my environment while doing so is proving a little tricky. Thankfully I should be able to dedicate a good bit of time over the next couple of days to this, so I hope to have an update soon.", "I've found a set of working rocm packages with clang-16 included, since I've switched to them I have gotten a different error, which I believe should be able to be fixed by adding the files somewhere in the bazel build system, I'm trying to figure out where exactly at the moment, but if there's anyone that could look at this that'd be appreciated.\r\n\r\n```\r\nINFO: Analyzed target //tensorflow/tools/pip_package:build_pip_package (624 packages loaded, 43746 targets configured).\r\nINFO: Found 1 target...\r\nERROR: /home/user/Repos/tensorflow/tensorflow/compiler/xla/stream_executor/rocm/BUILD:463:11: Compiling tensorflow/compiler/xla/stream_executor/rocm/rocm_helpers.cu.cc [for host] failed: undeclared inclusion(s) in rule '//tensorflow/compiler/xla/stream_executor/rocm:rocm_helpers':\r\nthis rule is missing dependency declarations for the following files included by 'tensorflow/compiler/xla/stream_executor/rocm/rocm_helpers.cu.cc':\r\n '/opt/rocm/llvm/lib/clang/16.0.0/include/__clang_hip_runtime_wrapper.h'\r\n '/opt/rocm/llvm/lib/clang/16.0.0/include/cuda_wrappers/cmath'\r\n '/opt/rocm/llvm/lib/clang/16.0.0/include/stddef.h'\r\n '/opt/rocm/llvm/lib/clang/16.0.0/include/__clang_hip_libdevice_declares.h'\r\n '/opt/rocm/llvm/lib/clang/16.0.0/include/__clang_hip_math.h'\r\n '/opt/rocm/llvm/lib/clang/16.0.0/include/cuda_wrappers/algorithm'\r\n '/opt/rocm/llvm/lib/clang/16.0.0/include/cuda_wrappers/new'\r\n '/opt/rocm/llvm/lib/clang/16.0.0/include/limits.h'\r\n '/opt/rocm/llvm/lib/clang/16.0.0/include/stdint.h'\r\n '/opt/rocm/llvm/lib/clang/16.0.0/include/__clang_hip_stdlib.h'\r\n '/opt/rocm/llvm/lib/clang/16.0.0/include/__clang_cuda_math_forward_declares.h'\r\n '/opt/rocm/llvm/lib/clang/16.0.0/include/__clang_hip_cmath.h'\r\n '/opt/rocm/llvm/lib/clang/16.0.0/include/__clang_cuda_complex_builtins.h'\r\n '/opt/rocm/llvm/lib/clang/16.0.0/include/cuda_wrappers/complex'\r\n '/opt/rocm/llvm/lib/clang/16.0.0/include/__stddef_max_align_t.h'\r\n '/opt/rocm/llvm/lib/clang/16.0.0/include/stdarg.h'\r\n/home/user/.cache/bazel/_bazel_user/057ab612123f87ae7f238751a7c28667/execroot/org_tensorflow/external/local_config_rocm/crosstool/clang/bin/crosstool_wrapper_driver_is_not_gcc:23: DeprecationWarning: 'pipes' is deprecated and slated for removal in Python 3.13\r\n import pipes\r\nclang-16: warning: argument unused during compilation: '-fcuda-flush-denormals-to-zero' [-Wunused-command-line-argument]\r\nTarget //tensorflow/tools/pip_package:build_pip_package failed to build\r\nUse --verbose_failures to see the command lines of failed build steps.\r\nINFO: Elapsed time: 27.215s, Critical Path: 6.75s\r\nINFO: 536 processes: 125 internal, 411 local.\r\nFAILED: Build did NOT complete successfully\r\n```", "I found that ROCm tensorflow-upstream goes further in the build process, so I'm looking at the differences at the moment to try to find a fix ", "I don't think there's a simple fix I can apply, and the best place for my issue is likely in the RadeonOpenCompute fork until the changes I need are merged. ", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61354\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61354\">No</a>\n" ]

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[ "Hi @rafrxx, so as I understand your situation, Your code runs but you're not sure if it ran correctly? It seems like you're running a Computer Vision model of some sort... are you trying to do classification or something else? What is the model's intended output? It seems like feature extraction -- in which case most humans probably can't interpret that. Any additional information will be helpful!", "Hello. Thank you for your response. This is an object detection model. I have a short code that displays the Frames Per Second (FPS) between captured frames. On a good phone without model inference, it runs at 80-100 FPS. When uncommenting the line responsible for model inference, the refresh rate drops to 8-10 FPS. When using GPU, it reaches 20-23 FPS. However, I'm unsure how to interpret the output.", "Hi @rafrxx, what is your exact model? or how did you build/architecture it? Where did you get the .tflite model from? It seems like it's just doing feature extraction and not doing the actual classification. Basically my current hypothesis is your tflite model is headless, it computes the features but doesn't have a classification head... but I am not sure how you created or got to that state.", "This model is based on ssd-mobilenet-v2-fpnlite-320 architecture. It provides output containing the locations, classes, and probabilities that I can utilize in the subsequent part of the code. I prepared the data myself, but I'm not as proficient in this area. It's possible that what I'm trying to achieve may not be feasible. Nevertheless, thank you for your response.", "Hi @rafrxx, no worries, what format are the expected outputs and what exactly are you seeing? It's kind of hard for me to help as I don't know how they look like and I don't have the model file. If you can upload a toy model, toy project or both that I can reproduce locally that'll be the most helpful.", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61367\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61367\">No</a>\n", "I also have the concern @rafrxx. I created a machine learning model for object detection. I got the code above from a sample code provided by android studio. Now, I am not sure on how to output the prediction. " ]

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[ "Hi @MogilipalemHemanthKumar \r\n\r\nWe need to add the select tf ops dependencies as mentioned error if the converted model has select ops.\r\n\r\nYou can specify this in your build.gradle dependencies by adding it alongside the standard TensorFlow Lite AAR as follows:\r\n```\r\ndependencies {\r\n implementation 'org.tensorflow:tensorflow-lite:0.0.0-nightly-SNAPSHOT'\r\n // This dependency adds the necessary TF op support.\r\n implementation 'org.tensorflow:tensorflow-lite-select-tf-ops:0.0.0-nightly-SNAPSHOT'\r\n}\r\n```\r\nThanks.", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61353\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61353\">No</a>\n" ]

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[ "@sronen71,\r\nAs you mentioned, it was the issue on tensorflow v2.12. When I tried to execute the same mentioned code on tensorflow v2.13`(tpu)` it was executed without any issue/error. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/349987540ae6320ed3726d2e86a996d4/untitled1255.ipynb). Thank you!", "That's great, thank you @tilakrayal !\r\n", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61352\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61352\">No</a>\n" ]

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[ "Appears to be fixed in nightly 2.14.0-dev20230721", "Hi @teddy661 ,\r\n\r\nI have replicated the reported behaviour with TF2.13 and python=3.11 and the reported error was observed. However with tf-nightly there is no such error which also same as per your observation. Attached below are the logs for checking same.\r\n\r\n[61351-logs.txt](https://github.com/tensorflow/tensorflow/files/12142597/61351-logs.txt)\r\n\r\n\r\nIt's already resolved in tf-nightly. We need to check whether the error is actually stopping from loading dataset. Could you please check whether the dataset actually loaded or not ? You can use the below code for checking.\r\n\r\n`ds_train.take(1).get_single_element()\r\n`", "It does appear to load the dataset and return the first element correctly. I'll try to run a model fit with it later to see if I'm getting the same results. I didn't want to move forward with it since I wasn't sure what exactly was going on with the entirety of the data. \r\n\r\nIn [2]: ds_train.take(1).get_single_element()\r\nOut[2]:\r\n({'attention_mask': <tf.Tensor: shape=(386,), dtype=int32, numpy=\r\n array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\r\n 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\r\n 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\r\n 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\r\n 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\r\n 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\r\n 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\r\n 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=int32)>,\r\n 'input_ids': <tf.Tensor: shape=(386,), dtype=int32, numpy=\r\n array([ 101, 2043, 2106, 20773, 2707, 3352, 2759, 1029, 102,\r\n 20773, 21025, 19358, 22815, 1011, 5708, 1006, 1013, 12170,\r\n 23432, 29715, 3501, 29678, 12325, 29685, 1013, 10506, 1011,\r\n 10930, 2078, 1011, 2360, 1007, 1006, 2141, 2244, 1018,\r\n 1010, 3261, 1007, 2003, 2019, 2137, 3220, 1010, 6009,\r\n 1010, 2501, 3135, 1998, 3883, 1012, 2141, 1998, 2992,\r\n 1999, 5395, 1010, 3146, 1010, 2016, 2864, 1999, 2536,\r\n 4823, 1998, 5613, 6479, 2004, 1037, 2775, 1010, 1998,\r\n 3123, 2000, 4476, 1999, 1996, 2397, 4134, 2004, 2599,\r\n 3220, 1997, 1054, 1004, 1038, 2611, 1011, 2177, 10461,\r\n 1005, 1055, 2775, 1012, 3266, 2011, 2014, 2269, 1010,\r\n 25436, 22815, 1010, 1996, 2177, 2150, 2028, 1997, 1996,\r\n 2088, 1005, 1055, 2190, 1011, 4855, 2611, 2967, 1997,\r\n 2035, 2051, 1012, 2037, 14221, 2387, 1996, 2713, 1997,\r\n 20773, 1005, 1055, 2834, 2201, 1010, 20754, 1999, 2293,\r\n 1006, 2494, 1007, 1010, 2029, 2511, 2014, 2004, 1037,\r\n 3948, 3063, 4969, 1010, 3687, 2274, 8922, 2982, 1998,\r\n 2956, 1996, 4908, 2980, 2531, 2193, 1011, 2028, 3895,\r\n 1000, 4689, 1999, 2293, 1000, 1998, 1000, 3336, 2879,\r\n 1000, 1012, 102, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0],\r\n dtype=int32)>,\r\n 'qas_id': <tf.Tensor: shape=(), dtype=string, numpy=b'56be85543aeaaa14008c9063'>,\r\n 'feature_index': <tf.Tensor: shape=(), dtype=int64, numpy=0>,\r\n 'token_type_ids': <tf.Tensor: shape=(386,), dtype=int32, numpy=\r\n array([0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\r\n 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\r\n 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\r\n 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\r\n 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\r\n 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\r\n 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\r\n 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=int32)>},\r\n {'end_positions': <tf.Tensor: shape=(), dtype=int64, numpy=78>,\r\n 'cls_index': <tf.Tensor: shape=(), dtype=int64, numpy=0>,\r\n 'is_impossible': <tf.Tensor: shape=(), dtype=int32, numpy=0>,\r\n 'start_positions': <tf.Tensor: shape=(), dtype=int64, numpy=75>,\r\n 'p_mask': <tf.Tensor: shape=(386,), dtype=int32, numpy=\r\n array([0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,\r\n 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\r\n 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\r\n 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\r\n 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\r\n 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\r\n 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\r\n 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\r\n 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\r\n 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\r\n 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=int32)>})\r\n", "Hi @teddy661 ,\r\n\r\nFor me it seems issue with TF2.13 which eventually resolved in tf-nightly.\r\n\r\nI have added `ds_train.take(1).get_single_element()` at last just to ensure dataset loaded. Because we are able to extract first element which indicates dataset is loaded indeed which addresses this issue.\r\n\r\nPlease check the training with tf-nightly and let us know if still have problem. If not please feel free to close the issue if resolved.\r\n\r\nThanks!", "Looks ok in tf-nightly", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61351\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61351\">No</a>\n", "Hi, I am unable to install tensorflow nightly. Any suggestions ?\r\n\r\nI am running tensor flow 2.13.1 ?" ]

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[ "@penpornk ", "Please help resolve conflicts / rebase as well. :)" ]

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[ "Hi @alankelly \r\nWill you have time to review this?\r\nThanks!", "Hi @alankelly Can you please review this PR ? Thank you!", "Hi @alankelly Can you please review this PR ? Thank you!", "This was indirectly fixed by https://github.com/tensorflow/tensorflow/commit/56edb7fc3b9b5e6f1eee637ac17b7fdce395503e#diff-9fc5de52478bebd19cc4b48121d9a65dc396a9f59e15968d27d4c29379021c2a\r\n\r\nClosing the PR." ]

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[ "Hi @xufuji456 \r\n\r\nThe ESRGAN expects the input shape of `50x50x3`, hence we need to resize the input tensor before allocating the tensors.\r\n```\r\n# Load TFLite model and allocate tensors.\r\ninterpreter = tf.lite.Interpreter(model_path=esrgan_model_path)\r\ninterpreter.resize_tensor_input(input_details[0]['index'],[1,50,50,3])\r\ninterpreter.allocate_tensors()\r\n```\r\nPlease find the [gist](https://colab.research.google.com/gist/pjpratik/e31c0319abbb9efb94b6c2c11af90537/61348.ipynb) modified to be comptatible for TF2.13 and let us know if helps.\r\n\r\nThanks. ", "@pjpratik Thank you for your reply.\r\n if I want to convert into 640x360 resolution, how should I do?\r\nI see here support re-convert model https://github.com/tensorflow/examples/blob/master/lite/examples/super_resolution/ml/super_resolution.ipynb", "Hi @xufuji456 \r\n\r\nThe ESRGAN produces x4 Super Resolution Image from images. If you want to convert to 640x360 resolution, you may need to pass 160x90 image and modify the input accordingly.\r\n```\r\nconcrete_func.inputs[0].set_shape([1, 160, 90, 3])\r\n```\r\nand after the conversion, accordingly we need to to resize the tensor \r\n```\r\ninterpreter.resize_tensor_input(input_details[0]['index'],[1,50,50,3])\r\n```\r\nand the pass the input image matching the size.\r\n\r\nThanks.\r\n", "Hello @pjpratik \r\nIt does work, thanks a lot. \r\nESRGAN has a good result, However, it takes a lot of times.\r\nIf I want to use SRGAN with x2 super resolution, for example, convert 640x360 into 1280x720.\r\nHow to convert the model into TensorFlow-lite, that running on platform of Android.", "Hi @xufuji456 \r\n\r\nGlad it worked.\r\n\r\nIf you would like to use SRGAN, you have convert SRGAN into TFLite model using different conversion options like `from_saved_model` format and `from_keras_model`.\r\n\r\nPlease refer to this [document](https://www.tensorflow.org/lite/models/convert/convert_models) on different ways of model for converting to TFLite.\r\n\r\nOnce the model is converted with specified model input dimesnions, it can be used for inference on Android.\r\n\r\nThanks.", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61348\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61348\">No</a>\n" ]

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[ "Hi @gymoon10 \r\n\r\nIn order to expedite the trouble-shooting process, please provide the complete code snippet to reproduce the issue reported.\r\n\r\nThank you.", "Hi @Varsha-anjanappa \r\n\r\nI edited the code part as you mentioned. There is no problem with compiling the model and applying the .fit() method, but an error is occurring when saving the trained model with h5 format. I also confirmed that there was no problem when I didn't use tf.Variable().\r\n\r\nThank you for your hard work.", "Hi @gymoon10 ,\r\n\r\nIf you are using custom layer and its __init__ method has `non_python` arguments then in order to make it serializable/deserializable you need to explicitly override get_config() and from_config() methods.\r\n\r\nPlease refer the attached [documentation](https://www.tensorflow.org/guide/keras/serialization_and_saving#custom_objects) for more details.\r\n\r\nYou need to change the code to something like below.\r\n\r\n```\r\[email protected]_keras_serializable()\r\nclass MDTA(keras.layers.Layer):\r\n '''***IMPORTANT*** - The channels must be zero when divided by num_heads'''\r\n def __init__(self, num_heads):\r\n super(MDTA, self).__init__()\r\n self.num_heads = num_heads\r\n self.temperature = tf.Variable([[[[1.]] for _ in range(self.num_heads)]], shape=[None, self.num_heads, 1, 1], trainable=True)\r\n\r\n def get_config(self):\r\n base_config = super().get_config()\r\n config = {\r\n \"temperature\": keras.saving.serialize_keras_object(self.temperature),\r\n }\r\n return {**base_config, **config}\r\n\r\n @classmethod\r\n def from_config(cls, config):\r\n custom_config = config.pop(\"temperature\")\r\n temperature = keras.saving.deserialize_keras_object(custom_config)\r\n return cls(sublayer, **config)\r\n```\r\nPlease try this and let us know.Thanks!\r\n\r\n", "@gymoon10 , Please make a note that the above code suitable for .`keras` format. ", "I want to save the full network, not the self.temperature alone", "@gymoon10 ,\r\n\r\nI have given an example of class MDTA. You need to do same for all subclassed Classes in your code in order to get the complete model saved. This is needed for subclassed models/layers only.", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61347\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61347\">No</a>\n" ]

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[ "All 3 CI failures are unrelated.\r\n* [Py+CPP Ubuntu CPU](https://source.cloud.google.com/results/invocations/3fdb42ac-da26-4a19-a735-e8f1aa45c9e8/log): Tests timed out.\r\n* [Py+CPP Ubuntu GPU](https://source.cloud.google.com/results/invocations/5280cdca-cf13-4318-a10c-2eb1b0beabaa/log): Tests timed out.\r\n* [AMD ROCm](http://ml-ci.amd.com:21096/blue/organizations/jenkins/tensorflow%2Fgithub-prs-upstream-master%2FAMD-ROCm-Community-CI-Build/detail/PR-61346/2/pipeline): Error unrelated to this PR.\r\n```\r\nAnalyzing: target //tensorflow/tools/pip_package:build_pip_package (648 packages loaded, 30206 targets configured)\r\n\r\nERROR: /workspace/tensorflow/compiler/xla/service/gpu/BUILD:853:11: in deps attribute of cc_library rule //tensorflow/compiler/xla/service/gpu:gpu_executable: Label '//tensorflow/tsl/platform:random' is duplicated\r\n\r\nERROR: /workspace/tensorflow/compiler/xla/service/gpu/BUILD:853:11: Analysis of target '//tensorflow/compiler/xla/service/gpu:gpu_executable' failed\r\n```" ]

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[ "@xiaxia-wang,\r\n\r\nI was able to clone the tensorflow repository without any problem on Ubuntu. I observed that you are using GCC 11, CUDA 12.1 and cuDNN 8.9 which is incompatible with TF v2.7.0. And also TF v2.7.0 is a pretty older version, please try to install the latest stable version.\r\n\r\nCould you please create a virtual environment and try to install the tensorflow as mentioned in this official document [link](https://www.tensorflow.org/install/source) and have a look at the [compatible](https://www.tensorflow.org/install/source#gpu) tested build configurations as well. Please find the attached screenshot for reference.\r\n\r\n\r\n", "Thanks for your reply. \r\n\r\nI was able to clone the tensorflow repository without any problem. The checkout process also works well. My problem arises in the bazel build process. I explicitly follow every step mentioned in the official document [link](https://www.tensorflow.org/install/source), but when I run `bazel build --config=cuda //tensorflow/tools/pip_package:build_pip_package`, it turns out as the issue provided above. \r\n\r\nI have also read the table of compatible tests, but it shows the latest tested TF-GPU version is 2.6.0. Could you please explain more about the incompatibility? Is my tensorflow too old for the cuda version? If I remember it correctly, higher cuda version is compatible with lower requirements.\r\n\r\n", "@xiaxia-wang,\r\nIs there any specific reason to install tensorflow v2.7, because as mentioned above v2.7 is the pretty older version. It's unlikely for TF 2.7 version to receive any bug fixes except when we have security patches. There is a high possibility that this was fixed with later TF versions. \r\n\r\nWe request please create a virtual environment and try to install the tensorflow latest stable v2.13 as mentioned in this official document [link](https://www.tensorflow.org/install/source) and have a look at the [compatible](https://www.tensorflow.org/install/source#gpu) tested build configurations as well. Thank you!", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61345\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61345\">No</a>\n", "Hi! i'm encountering the exact issue, may i ask have you resolved this?", "Unfortunately, no. They only told me to use newer versions of tensorflow...In the end I turned to pytorch and reconstructed my program, which for me is better and easier to use." ]

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[ "see also: #35084 and #51839 (both closed, but the issue re-appeared in 2.13.0)", "Hi @Pyrestone ,\r\n\r\nI have replicated the reported behaviour and attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/84a602e1d03dd977317080607592bd0a/61344_-tf_memory_leak_py_function.ipynb) for reference. \r\n\r\nI can observe memory increased from batch:0 to batch:3600. Needs to be investigated.", "Note for people finding this issue in the meantime:\r\n\r\nAs a temporary (or permanent I suppose) **workaround** is using `tf.numpy_function` instead of `tf.py_function` in tf.data.Dataset.map(). \r\n\r\nIt behaves very similarly, except the function then receives and returns numpy arrays instead of eager tensors. \r\nThe only other notable difference is that `tf.numpy_function` doesn't support gradient computation (which probably shouldn't matter in a dataset.map() call).", "@Pyrestone Thanks for your solution." ]

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[ "@soudabehmousavi99 If 16x8 quantization is not supported for some operators in the model, then the model still can be quantized, but unsupported operators kept in float. The following option should be added to the target_spec to allow this.\r\n\r\n```\r\nimport tensorflow as tf\r\nconverter = tf.lite.TFLiteConverter.from_saved_model(saved_model_dir)\r\nconverter.representative_dataset = representative_dataset\r\nconverter.optimizations = [tf.lite.Optimize.DEFAULT]\r\nconverter.target_spec.supported_ops = [tf.lite.OpsSet.EXPERIMENTAL_TFLITE_BUILTINS_ACTIVATIONS_INT16_WEIGHTS_INT8,\r\ntf.lite.OpsSet.TFLITE_BUILTINS]\r\ntflite_quant_model = converter.convert()\r\n```\r\n\r\nCould you please have a look at [this](https://www.tensorflow.org/lite/performance/post_training_quantization) doc and let us know if it helps?\r\nThank you!", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61343\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61343\">No</a>\n" ]

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[ "Check out this pull request on&nbsp; <a href=\"https://app.reviewnb.com/tensorflow/tensorflow/pull/61342\"><img align=\"absmiddle\" alt=\"ReviewNB\" height=\"28\" class=\"BotMessageButtonImage\" src=\"https://raw.githubusercontent.com/ReviewNB/support/master/images/button_reviewnb.png\"/></a> \n\n See visual diffs & provide feedback on Jupyter Notebooks. \n\n---\n\n <i>Powered by <a href='https://www.reviewnb.com/?utm_source=gh'>ReviewNB</a></i>", "Hi @alankelly Can you please review this PR ? Thank you!", "Hi @alankelly Can you please review this PR ? Thank you!", "Hi @alankelly Can you please review this PR ? Thank you!", "Hi @alankelly Can you please review this PR ? Thank you!", "Hi @alankelly Can you please review this PR ? Thank you!", "Hi @alankelly Can you please review this PR ? Thank you!", "Hi @renjie-liu Can you please review this PR ? Thank you!\r\n\r\n", "Hi @renjie-liu Can you please review this PR ? Thank you!", "Hi @renjie-liu Can you please review this PR ? Thank you!", "Hi @alankelly Can you please review this PR ? Thank you!", "Hi @alankelly Can you please review this PR ? Thank you!", "Hi @alankelly Can you please review this PR ? Thank you!", "Hi @pkgoogle Can you please resolve conflicts? Thank you!", "This PR is stale because it has been open for 14 days with no activity. It will be closed if no further activity occurs. Thank you.", "This PR was closed because it has been inactive for 14 days since being marked as stale. Please reopen if you'd like to work on this further." ]

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[ "@sun1638650145 Please try to use the new version of XCode 14.3 to include an updated version of the linker ld. \r\nCould you refer to this Commit: https://github.com/tensorflow/tensorflow/commit/55939be39409283c4b35a62049fe548a2de3ce44 and let us know if it would work for you as a solved environment? Thank you!", "@sushreebarsa Hello, I am well aware that this is not caused by `ld` because I have already addressed this issue when building previous versions (as documented in this [issue](https://github.com/tensorflow/tensorflow/issues/57914)).", "Hi @sun1638650145 ,\r\n\r\nIf I am not wrong Xcode 14.3 resolved build issue last time in which you also involved and confirmed.Could you please confirm whether TF2.13 build fails with Xcode 14.3 also ?\r\n\r\n", "@SuryanarayanaY Yes, after using `Xcode 14.3`, I was able to build `tensorflow 2.12` successfully. However, now when trying to build `tensorflow 2.13`, a different error occurs (the log information provided above). I'm not sure if this issue is caused by Xcode or something else.", "@sun1638650145 ,\r\n\r\nCould you please confirm TF2.13 build was failing even with XCode 14.3 since I can see in the template you are using 14.0.3.\r\n\r\nI have tried build with 14.0.3 and replicated the reported build failure.Logas attached below for reference. I couldn't update it 14.3.x due to access related issues.\r\n\r\n```\r\n(base) suryanarayanay-macbookpro:tensorflow suryanarayanay$ bazel build //tensorflow/tools/pip_package:build_pip_package\r\nExtracting Bazel installation...\r\nStarting local Bazel server and connecting to it...\r\nINFO: Options provided by the client:\r\n Inherited 'common' options: --isatty=1 --terminal_columns=157\r\nINFO: Reading rc options for 'build' from /Users/suryanarayanay/tensorflow/.bazelrc:\r\n Inherited 'common' options: --experimental_repo_remote_exec\r\nINFO: Reading rc options for 'build' from /Users/suryanarayanay/tensorflow/.bazelrc:\r\n 'build' options: --define framework_shared_object=true --define tsl_protobuf_header_only=true --define=use_fast_cpp_protos=true --define=allow_oversize_protos=true --spawn_strategy=standalone -c opt --announce_rc --define=grpc_no_ares=true --noincompatible_remove_legacy_whole_archive --enable_platform_specific_config --define=with_xla_support=true --config=short_logs --config=v2 --define=no_aws_support=true --define=no_hdfs_support=true --experimental_cc_shared_library --experimental_link_static_libraries_once=false --incompatible_enforce_config_setting_visibility\r\nINFO: Reading rc options for 'build' from /Users/suryanarayanay/tensorflow/.tf_configure.bazelrc:\r\n 'build' options: --action_env PYTHON_BIN_PATH=/opt/homebrew/opt/[email protected]/bin/python3.11 --action_env PYTHON_LIB_PATH=/opt/homebrew/opt/[email protected]/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages --python_path=/opt/homebrew/opt/[email protected]/bin/python3.11\r\nINFO: Reading rc options for 'build' from /Users/suryanarayanay/tensorflow/.bazelrc:\r\n 'build' options: --deleted_packages=tensorflow/compiler/mlir/tfrt,tensorflow/compiler/mlir/tfrt/benchmarks,tensorflow/compiler/mlir/tfrt/jit/python_binding,tensorflow/compiler/mlir/tfrt/jit/transforms,tensorflow/compiler/mlir/tfrt/python_tests,tensorflow/compiler/mlir/tfrt/tests,tensorflow/compiler/mlir/tfrt/tests/ir,tensorflow/compiler/mlir/tfrt/tests/analysis,tensorflow/compiler/mlir/tfrt/tests/jit,tensorflow/compiler/mlir/tfrt/tests/lhlo_to_tfrt,tensorflow/compiler/mlir/tfrt/tests/lhlo_to_jitrt,tensorflow/compiler/mlir/tfrt/tests/tf_to_corert,tensorflow/compiler/mlir/tfrt/tests/tf_to_tfrt_data,tensorflow/compiler/mlir/tfrt/tests/saved_model,tensorflow/compiler/mlir/tfrt/transforms/lhlo_gpu_to_tfrt_gpu,tensorflow/core/runtime_fallback,tensorflow/core/runtime_fallback/conversion,tensorflow/core/runtime_fallback/kernel,tensorflow/core/runtime_fallback/opdefs,tensorflow/core/runtime_fallback/runtime,tensorflow/core/runtime_fallback/util,tensorflow/core/tfrt/eager,tensorflow/core/tfrt/eager/backends/cpu,tensorflow/core/tfrt/eager/backends/gpu,tensorflow/core/tfrt/eager/core_runtime,tensorflow/core/tfrt/eager/cpp_tests/core_runtime,tensorflow/core/tfrt/gpu,tensorflow/core/tfrt/run_handler_thread_pool,tensorflow/core/tfrt/runtime,tensorflow/core/tfrt/saved_model,tensorflow/core/tfrt/graph_executor,tensorflow/core/tfrt/saved_model/tests,tensorflow/core/tfrt/tpu,tensorflow/core/tfrt/utils\r\nINFO: Found applicable config definition build:short_logs in file /Users/suryanarayanay/tensorflow/.bazelrc: --output_filter=DONT_MATCH_ANYTHING\r\nINFO: Found applicable config definition build:v2 in file /Users/suryanarayanay/tensorflow/.bazelrc: --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1\r\nINFO: Found applicable config definition build:macos in file /Users/suryanarayanay/tensorflow/.bazelrc: --apple_platform_type=macos --copt=-DGRPC_BAZEL_BUILD --copt=-w --define=PREFIX=/usr --define=LIBDIR=$(PREFIX)/lib --define=INCLUDEDIR=$(PREFIX)/include --define=PROTOBUF_INCLUDE_PATH=$(PREFIX)/include --cxxopt=-std=c++17 --host_cxxopt=-std=c++17\r\nWARNING: Download from https://storage.googleapis.com/mirror.tensorflow.org/github.com/tensorflow/runtime/archive/0aaa6e679847a4eeb407136e7b0bcef93ec652e6.tar.gz failed: class java.io.FileNotFoundException GET returned 404 Not Found\r\nWARNING: Download from https://storage.googleapis.com/mirror.tensorflow.org/github.com/llvm/llvm-project/archive/a52054cfa29d665c43141c66c20a7b8f7a96b546.tar.gz failed: class java.io.FileNotFoundException GET returned 404 Not Found\r\nWARNING: Download from https://storage.googleapis.com/mirror.tensorflow.org/github.com/google/benchmark/archive/f7547e29ccaed7b64ef4f7495ecfff1c9f6f3d03.tar.gz failed: class java.io.FileNotFoundException GET returned 404 Not Found\r\nWARNING: Download from https://mirror.bazel.build/github.com/bazelbuild/rules_cc/archive/081771d4a0e9d7d3aa0eed2ef389fa4700dfb23e.tar.gz failed: class java.io.FileNotFoundException GET returned 404 Not Found\r\nWARNING: Download from https://storage.googleapis.com/mirror.tensorflow.org/github.com/google/boringssl/archive/c00d7ca810e93780bd0c8ee4eea28f4f2ea4bcdc.tar.gz failed: class java.io.FileNotFoundException GET returned 404 Not Found\r\nWARNING: Download from https://storage.googleapis.com/mirror.tensorflow.org/github.com/google/XNNPACK/archive/7adae8e6ded8fff33d92212ca1028d2419cd34d4.zip failed: class java.io.FileNotFoundException GET returned 404 Not Found\r\nWARNING: Download from https://storage.googleapis.com/mirror.tensorflow.org/github.com/openxla/stablehlo/archive/458bdd95771e9861e6488868e315a1b0340058ba.zip failed: class java.io.FileNotFoundException GET returned 404 Not Found\r\nWARNING: Download from https://storage.googleapis.com/mirror.tensorflow.org/github.com/pybind/pybind11_abseil/archive/2c4932ed6f6204f1656e245838f4f5eae69d2e29.tar.gz failed: class java.io.FileNotFoundException GET returned 404 Not Found\r\nINFO: Analyzed target //tensorflow/tools/pip_package:build_pip_package (606 packages loaded, 43912 targets configured).\r\nINFO: Found 1 target...\r\nERROR: /Users/suryanarayanay/tensorflow/tensorflow/BUILD:1128:21: declared output 'tensorflow/libtensorflow_framework.2.dylib' was not created by genrule. This is probably because the genrule actually didn't create this output, or because the output was a directory and the genrule was run remotely (note that only the contents of declared file outputs are copied from genrules run remotely)\r\nERROR: /Users/suryanarayanay/tensorflow/tensorflow/BUILD:1128:21: Executing genrule //tensorflow:libtensorflow_framework.2.dylib_sym failed: not all outputs were created or valid\r\nrealpath: illegal option -- -\r\nusage: realpath [-q] [path ...]\r\nTarget //tensorflow/tools/pip_package:build_pip_package failed to build\r\nUse --verbose_failures to see the command lines of failed build steps.\r\nINFO: Elapsed time: 448.591s, Critical Path: 28.99s\r\nINFO: 6015 processes: 2038 internal, 3977 local.\r\nFAILED: Build did NOT complete successfully\r\n(base) suryanarayanay-macbookpro:tensorflow suryanarayanay$ ld -v\r\n@(#)PROGRAM:ld PROJECT:ld64-857.1\r\nBUILD 23:13:29 May 7 2023\r\nconfigured to support archs: armv6 armv7 armv7s arm64 arm64e arm64_32 i386 x86_64 x86_64h armv6m armv7k armv7m armv7em\r\nLTO support using: LLVM version 14.0.3, (clang-1403.0.22.14.1) (static support for 29, runtime is 29)\r\nTAPI support using: Apple TAPI version 14.0.3 (tapi-1403.0.5.1)\r\n(base) suryanarayanay-macbookpro:tensorflow suryanarayanay$ \r\n```\r\n\r\n\r\n\r\n", "@SuryanarayanaY Yes, I can confirm that my current Xcode and Command Line Tools versions are `14.3.1`, and they include clang version `14.0.3`. (There is currently no clang `14.3`.)", "@nitins17 , Could you please have a look into the issue. Thanks!", "It is likely still picking up the macOS version of realpath. See https://github.com/tensorflow/tensorflow/issues/60088#issuecomment-1499794693 ", "@sushreebarsa @SuryanarayanaY @nitins17 Thank you for your assistance. It was indeed due to the use of `realpath` that caused this compilation error. I have now resolved this issue.", "It is necessary to install `coreutils` and configure environment variables before proceeding with the official tutorial. I have placed the my tutorial [here](https://github.com/sun1638650145/Libraries-and-Extensions-for-TensorFlow-for-Apple-Silicon/blob/main/tutorials/tensorflow/tensorflow.md), hoping it can also assist those in need.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61341\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61341\">No</a>\n" ]

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[ "@lmx666-gif,\r\nI tried to execute the mentioned code and it was failing with the different error. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/5f35a72261875affb691a5549eaa5ca3/untitled1250.ipynb) and provide the complete code, dependencies and the tensorflow version you are using which helps to analyse the issue in an effective way. \r\n\r\n**tfds** is compatible with both eager and graph modes, but that doesn't mean they can be used the same way in either context.\r\n If you want a tensor representation of the inputs, you can use **outputs = dataset.make_one_shot_iterator().get_next().**\r\nAlso when you apply the `tf.function` decorator to a Python function that uses `tf.data.Dataset`, Tensorflow 2.0 will automatically convert the Python function into a graph that can be optimized for performance. This can lead to significant performance improvements, especially when working with large datasets.\r\nThank you!", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "Closing this issue as stale. Please reopen if this is still a valid request. Thank you!" ]

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[ "Hi @zhumakhan ,\r\n\r\nAFAIK, tf.Tensor has no method for getting pointer directly. But we have method called `ref` which can return a hashable reference object to the Tensor. Could you please check the source [here](https://www.tensorflow.org/api_docs/python/tf/Tensor#ref) for more details and let us know whether this can solve your purpose.\r\n\r\nThanks!", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further." ]

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[ "facing this on macOS too. Any solutions for this?", "@ljmerza There are a few dependencies like numpy that need a specific version installation. Did you try to upgrade the numpy version by using` pip install numpy==1.23.4`. Could you also use colab fallback as a workaround?\r\nThank you!\r\n", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "@sushreebarsa I tried your fix and I'm still running into same error", "Hi @ljmerza @temilolafaith \r\n\r\nThere is a known issue of tflite model maker installation if you are using python >=3.10. Please use Python 3.9 or [Mediapipe Model Maker](https://developers.google.com/mediapipe/solutions/model_maker) as a workaround.\r\n\r\nThanks.", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "> Hi @ljmerza @temilolafaith\r\n> \r\n> There is a known issue of tflite model maker installation if you are using python >=3.10. Please use Python 3.9 or [Mediapipe Model Maker](https://developers.google.com/mediapipe/solutions/model_maker) as a workaround.\r\n> \r\n> Thanks.\r\n\r\nAlso mediapipe-model-maker is broken. On windows I get this error during setup when it starts to get PyYAML dependency\r\n\r\n```\r\nDownloading PyYAML-5.4.1.tar.gz (175 kB)\r\n ---------------------------------------- 175.1/175.1 kB 2.6 MB/s eta 0:00:00\r\n Installing build dependencies ... done\r\n Getting requirements to build wheel ... error\r\n error: subprocess-exited-with-error\r\n\r\n × Getting requirements to build wheel did not run successfully.\r\n\r\n...\r\n\\Lib\\site-packages\\setuptools\\_distutils\\cmd.py\", line 107, in __getattr__\r\n raise AttributeError(attr)\r\n AttributeError: cython_sources\r\n [end of output]\r\n```", "Hi @JohnFarl \r\n\r\nCould you please try again as I was successfully able to install mediapipe model maker on ubuntu(colab). Please find the [gist](https://colab.research.google.com/gist/pjpratik/dbb294b5537ee15bde72ca1471f0d834/61337.ipynb).\r\n\r\nThanks.", "> Hi @JohnFarl\r\n> \r\n> Could you please try again as I was successfully able to install mediapipe model maker on ubuntu(colab). Please find the [gist](https://colab.research.google.com/gist/pjpratik/dbb294b5537ee15bde72ca1471f0d834/61337.ipynb).\r\n> \r\n> Thanks.\r\n\r\nI confirm that mediapipe model maker installation fails on Windows with the error mentioned above. (Python 3.11)", "Hi @ljmerza, I was able to install if I downgraded Python to 3.9.17, can you try that out to see if you are able to continue that way?\r\n\r\nIf you are using conda you can do so like this:\r\n```py\r\nconda create -n your_env_name python=3.9\r\nconda activate your_env_name\r\npip install -q tflite-model-maker\r\n```", "> Hi @ljmerza @temilolafaith\r\n> \r\n> There is a known issue of tflite model maker installation if you are using python >=3.10. Please use Python 3.9 or [Mediapipe Model Maker](https://developers.google.com/mediapipe/solutions/model_maker) as a workaround.\r\n> \r\n> Thanks.\r\n\r\nDowngraded from 3.10.9 to Python 3.9.6. \r\nStill experiencing the same error.\r\n\r\n% python3 --version\r\nPython 3.9.6", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61337\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61337\">No</a>\n", "Problem is still here unsolved. This automatic issue close should be disabled or set to a reasonable amount of time. ", "Hi @lu-wang-g, assigning this to you to consolidate tflite-model-maker issues. Thanks.", "I got the same issue and was able to fix by using this [hack](https://discuss.python.org/t/getting-requirements-to-build-wheel-did-not-run-successfully-exit-code-1/30365/2):\r\n\r\n```bash\r\necho \"Cython<3\" > cython_constraint.txt\r\n$ PIP_CONSTRAINT=cython_constraint.txt pip install \"tflite-model-maker\"\r\n```", "I was able to solve that dependency issue by installing `cython` and `pyyaml` before installing the model maker, based on [that answer on Stackoverflow](https://stackoverflow.com/a/77491847/1181162), with:\r\n```\r\n$ pip install \"cython<3.0.0\" wheel\r\n$ pip install \"pyyaml==5.4.1\" // pip install \"pyyaml<6.0\" probably also works\r\n```\r\n\r\nUntil the next dependency conflict...", "What ended up working for me was a combination of @bduyng and @vpmalley's solutions.\r\n\r\nThis is what I did, in order\r\n```\r\npip install \"cython<3.0.0\" wheel // succeeded\r\n\r\npip install \"pyyaml==5.4.1\" // failed with the same error\r\n\r\necho \"Cython<3\" > cython_constraint.txt\r\nPIP_CONSTRAINT=cython_constraint.txt pip install \"pyyaml==5.4.1\" // succeeded\r\n\r\nPIP_CONSTRAINT=cython_constraint.txt pip install \"tflite-model-maker\" // failed with the same error\r\n\r\npip install \"tflite-model-maker\" // worked!\r\n```" ]

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[ "Hi @kurkurzz ,\r\n\r\nPlease provide the complete code snippet to reproduce the issue reported. I've tried to reproduce the issue [here](https://colab.research.google.com/gist/Varsha-anjanappa/6855e9a19e74be64edca3d3d45a85121/61336_v1.ipynb), please provide the dataset or dummy data.\r\n\r\nThank you!!", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "I am having the same issue but with tracking MSE. This is also seen by others (https://stackoverflow.com/questions/76701617/the-following-arguments-are-not-supported-with-the-native-keras-format-opti). Appears to be related to ModelCheckpoint and passing an \"options\" argument which should no longer be present in the latest version of Keras.", "Hello,\r\nThis issue occurs when you try to use the F1Score metric with the default value for the weight argument in a multi-class or multi-label problem. This works only for binary classification problems. To make this work, you need to add 'macro' or 'weighted' depending on your data class imbalance in the F1Score instance during model compilation. \r\nThis should fix the issue. ", "Hi @kurkurzz ,\r\n\r\nIn the current implementation,for F1-score metric if we go with default value for argument `average = None` and the use case is either `multi-class classification` or `multi-label classification` then this problem occurs. With all other options like `average = 'weighted'` or `'micro'` or `'macro'` it works as intended. Attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/b183243a24924ebe56c63c7622288587/f1_score.ipynb) for reference.\r\n\r\nIf `average=None`, no averaging is performed and `result()` will return the score for each class which is causing the error.", "Hi @kurkurzz ,\r\n\r\nSince monitor = val_f1_score returns f1_score for each class when averaging=None, it is not possible to `save_best_only` model as it it ambiguous to compare the arrays of multiple values which is best.Hence the error but ambiguous.\r\n\r\nFor this case I am trying to propose a PR to fall back to `save_best_only = False` instead of raising an exception." ]

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[ "Hi @ezhulenev, Can you please take a look at this PR? Thank you!" ]

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[ "Hi @ezhulenev, can you please take a look at this PR? Thanks!", "Hi @ezhulenev, Can you please take a look at this PR? Thanks!", "Hi @ezhulenev - this PR got reverted, can you please point me to the error so we can fix it? Thanks." ]

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[ "Hi @anand-shubham ,\r\n\r\nPlease find the tested configurations of CUDA and cuDNN [here](https://www.tensorflow.org/install/source#gpu).\r\n\r\n\r\ntensorflow-2.4.0 | 3.6-3.8 | GCC 7.3.1 | Bazel 3.1.0 | 8.0 | 11.0\r\n-- | -- | -- | -- | -- | --\r\n\r\nFor TF2.4v you should use CUDA 11.0 and cuDNN 8.0 and GCC versions should be 7.3.1.\r\n\r\nPlease upgrade GCC to 7.3.1 and downgrade CUDA and cuDNN to above mentioned configurations because we can't assure backward compatibility of latest CUDA/cuDNN for older TF versions.\r\n\r\nPlease do above changes and let us know if still having problem.\r\n\r\nThanks!\r\n\r\n", "Hi @SuryanarayanaY,\r\n\r\nWhile installing the Nvidia driver we have an option to select the CUDA TOOLKIT version. Does that affect the CUDA, cuDNN we install later?\r\nAs I have already tried with the For TF2.4v you should use CUDA 11.0 and cuDNN 8.0.\r\n\r\nThe GPU installed is Tesla V100 Volta Architecture. \r\n\r\nCurrent RHEL Version :Red Hat Enterprise Linux Server release 7.9 (Maipo) \r\n\r\nCurrent GCC Verison: gcc (GCC) 4.8.5 20150623 (Red Hat 4.8.5-44) .", "Hi @anand-shubham ,\r\n\r\nAs per official documentation it recommends to install CUDA and cuDNN toolkits using Conda explicitly. As you are using TF2.4 version which is quite older versions which we are not supporting now.Also official documentation keeping only the instructions for latest TF release which you can find [here](https://www.tensorflow.org/install/pip#step-by-step_instructions). Please note that the attached instructions are applicable to latest TF version which is Tf2.13 now.\r\n\r\nI am attaching the instructions which I have used for Tf2.11 .\r\n\r\n```\r\nconda install -c conda-forge cudatoolkit=11.2 cudnn=8.1.0\r\nexport LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib/\r\nmkdir -p $CONDA_PREFIX/etc/conda/activate.d\r\necho 'export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib/' > $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh\r\n```\r\nYou can try replacing with `cudatoolkit=11.0 cudnn=8.0` and follow same instructions for path setting mentioned above.\r\n\r\nFirst you need to install GPU driver and verify it using `nvidia-smi` command and then follow the instructions mentioned above for CUDA/cuDNN toolkit installation and path setting.\r\n\r\nCould you also please check your GPU compatibility from Nvidia website may be from [here](https://developer.nvidia.com/cuda-gpus). \r\n\r\nBut we recommend to use latest TF versions that can be better supported by us. Thanks!\r\n", "2023-07-24 18:29:10.026158: I tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:428] Loaded cuDNN version 8100\r\n2023-07-24 18:29:40.514393: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:219] failed to create cublas handle: cublas error\r\n2023-07-24 18:29:40.514447: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:221] Failure to initialize cublas may be due to OOM (cublas needs some free memory when you initialize it, and your deep-learning framework may have preallocated more than its fair share), or may be because this binary was not built with support for the GPU in your machine.\r\n2023-07-24 18:29:40.514769: W tensorflow/core/framework/op_kernel.cc:1830] OP_REQUIRES failed at conv_ops_fused_impl.h:621 : NOT_FOUND: No algorithm worked! Error messages:\r\n Profiling failure on CUDNN engine 1#TC: UNKNOWN: CUDNN_STATUS_EXECUTION_FAILED\r\nin tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc(5150): 'status'\r\n Profiling failure on CUDNN engine 1: UNKNOWN: CUDNN_STATUS_EXECUTION_FAILED\r\nin tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc(5150): 'status'\r\n Profiling failure on CUDNN engine 0#TC: UNKNOWN: CUDNN_STATUS_EXECUTION_FAILED\r\nin tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc(5150): 'status'\r\n Profiling failure on CUDNN engine 0: UNKNOWN: CUDNN_STATUS_EXECUTION_FAILED\r\nin tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc(5150): 'status'\r\n Profiling failure on CUDNN engine 2#TC: UNKNOWN: CUDNN_STATUS_EXECUTION_FAILED\r\nin tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc(5150): 'status'\r\n Profiling failure on CUDNN engine 2: UNKNOWN: CUDNN_STATUS_EXECUTION_FAILED\r\nin tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc(5150): 'status'\r\n Profiling failure on CUDNN engine 4#TC: UNKNOWN: CUDNN_STATUS_INTERNAL_ERROR\r\nin tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc(5150): 'status'\r\n Profiling failure on CUDNN engine 4: UNKNOWN: CUDNN_STATUS_INTERNAL_ERROR\r\nin tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc(5150): 'status'\r\n Profiling failure on CUDNN engine 6#TC: UNKNOWN: CUDNN_STATUS_EXECUTION_FAILED\r\nin tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc(5150): 'status'\r\n Profiling failure on CUDNN engine 6: UNKNOWN: CUDNN_STATUS_EXECUTION_FAILED\r\nin tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc(5150): 'status'\r\n Profiling failure on CUDNN engine 5#TC: UNKNOWN: CUDNN_STATUS_INTERNAL_ERROR\r\nin tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc(5150): 'status'\r\n Profiling failure on CUDNN engine 5: UNKNOWN: CUDNN_STATUS_INTERNAL_ERROR\r\nin tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc(5150): 'status'\r\n Profiling failure on CUDNN engine 7#TC: UNKNOWN: CUDNN_STATUS_EXECUTION_FAILED\r\nin tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc(5150): 'status'\r\n Profiling failure on CUDNN engine 7: UNKNOWN: CUDNN_STATUS_EXECUTION_FAILED\r\nin tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc(5150): 'status'\r\nTraceback (most recent call last):\r\n File \"/data01/PatternClassClassification/Model_2_PatternClass_imgSize150x150.py\", line 366, in <module>\r\n history = stn_model.fit(\r\n File \"/data01/Software_installations/Anaconda3/envs/gpu_env/lib/python3.10/site-packages/keras/utils/traceback_utils.py\", line 70, in error_handler\r\n raise e.with_traceback(filtered_tb) from None\r\n File \"/data01/Software_installations/Anaconda3/envs/gpu_env/lib/python3.10/site-packages/tensorflow/python/eager/execute.py\", line 52, in quick_execute\r\n tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\r\ntensorflow.python.framework.errors_impl.NotFoundError: Graph execution error:\r\n\r\nDetected at node 'model_1/conv2d_5/Relu' defined at (most recent call last):\r\n File \"/data01/PatternClassClassification/Model_2.py\", line 366, in <module>\r\n history = stn_model.fit(\r\n File \"/data01/Software_installations/Anaconda3/envs/gpu_env/lib/python3.10/site-packages/keras/utils/traceback_utils.py\", line 65, in error_handler\r\n return fn(*args, **kwargs)\r\n File \"/data01/Software_installations/Anaconda3/envs/gpu_env/lib/python3.10/site-packages/keras/engine/training.py\", line 1650, in fit\r\n tmp_logs = self.train_function(iterator)\r\n File \"/data01/Software_installations/Anaconda3/envs/gpu_env/lib/python3.10/site-packages/keras/engine/training.py\", line 1249, in train_function\r\n return step_function(self, iterator)\r\n File \"/data01/Software_installations/Anaconda3/envs/gpu_env/lib/python3.10/site-packages/keras/engine/training.py\", line 1233, in step_function\r\n outputs = model.distribute_strategy.run(run_step, args=(data,))\r\n File \"/data01/Software_installations/Anaconda3/envs/gpu_env/lib/python3.10/site-packages/keras/engine/training.py\", line 1222, in run_step\r\n outputs = model.train_step(data)\r\n File \"/data01/Software_installations/Anaconda3/envs/gpu_env/lib/python3.10/site-packages/keras/engine/training.py\", line 1023, in train_step\r\n y_pred = self(x, training=True)\r\n File \"/data01/Software_installations/Anaconda3/envs/gpu_env/lib/python3.10/site-packages/keras/utils/traceback_utils.py\", line 65, in error_handler\r\n return fn(*args, **kwargs)\r\n File \"/data01/Software_installations/Anaconda3/envs/gpu_env/lib/python3.10/site-packages/keras/engine/training.py\", line 561, in __call__\r\n return super().__call__(*args, **kwargs)\r\n File \"/data01/Software_installations/Anaconda3/envs/gpu_env/lib/python3.10/site-packages/keras/utils/traceback_utils.py\", line 65, in error_handler\r\n return fn(*args, **kwargs)\r\n File \"/data01/Software_installations/Anaconda3/envs/gpu_env/lib/python3.10/site-packages/keras/engine/base_layer.py\", line 1132, in __call__\r\n outputs = call_fn(inputs, *args, **kwargs)\r\n File \"/data01/Software_installations/Anaconda3/envs/gpu_env/lib/python3.10/site-packages/keras/utils/traceback_utils.py\", line 96, in error_handler\r\n return fn(*args, **kwargs)\r\n File \"/data01/Software_installations/Anaconda3/envs/gpu_env/lib/python3.10/site-packages/keras/engine/functional.py\", line 511, in call\r\n return self._run_internal_graph(inputs, training=training, mask=mask)\r\n File \"/data01/Software_installations/Anaconda3/envs/gpu_env/lib/python3.10/site-packages/keras/engine/functional.py\", line 668, in _run_internal_graph\r\n outputs = node.layer(*args, **kwargs)\r\n File \"/data01/Software_installations/Anaconda3/envs/gpu_env/lib/python3.10/site-packages/keras/utils/traceback_utils.py\", line 65, in error_handler\r\n return fn(*args, **kwargs)\r\n File \"/data01/Software_installations/Anaconda3/envs/gpu_env/lib/python3.10/site-packages/keras/engine/base_layer.py\", line 1132, in __call__\r\n outputs = call_fn(inputs, *args, **kwargs)\r\n File \"/data01/Software_installations/Anaconda3/envs/gpu_env/lib/python3.10/site-packages/keras/utils/traceback_utils.py\", line 96, in error_handler\r\n return fn(*args, **kwargs)\r\n File \"/data01/Software_installations/Anaconda3/envs/gpu_env/lib/python3.10/site-packages/keras/layers/convolutional/base_conv.py\", line 314, in call\r\n return self.activation(outputs)\r\n File \"/data01/Software_installations/Anaconda3/envs/gpu_env/lib/python3.10/site-packages/keras/activations.py\", line 317, in relu\r\n return backend.relu(\r\n File \"/data01/Software_installations/Anaconda3/envs/gpu_env/lib/python3.10/site-packages/keras/backend.py\", line 5369, in relu\r\n x = tf.nn.relu(x)\r\nNode: 'model_1/conv2d_5/Relu'\r\nNo algorithm worked! Error messages:\r\n Profiling failure on CUDNN engine 1#TC: UNKNOWN: CUDNN_STATUS_EXECUTION_FAILED\r\nin tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc(5150): 'status'\r\n Profiling failure on CUDNN engine 1: UNKNOWN: CUDNN_STATUS_EXECUTION_FAILED\r\nin tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc(5150): 'status'\r\n Profiling failure on CUDNN engine 0#TC: UNKNOWN: CUDNN_STATUS_EXECUTION_FAILED\r\nin tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc(5150): 'status'\r\n Profiling failure on CUDNN engine 0: UNKNOWN: CUDNN_STATUS_EXECUTION_FAILED\r\nin tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc(5150): 'status'\r\n Profiling failure on CUDNN engine 2#TC: UNKNOWN: CUDNN_STATUS_EXECUTION_FAILED\r\nin tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc(5150): 'status'\r\n Profiling failure on CUDNN engine 2: UNKNOWN: CUDNN_STATUS_EXECUTION_FAILED\r\nin tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc(5150): 'status'\r\n Profiling failure on CUDNN engine 4#TC: UNKNOWN: CUDNN_STATUS_INTERNAL_ERROR\r\nin tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc(5150): 'status'\r\n Profiling failure on CUDNN engine 4: UNKNOWN: CUDNN_STATUS_INTERNAL_ERROR\r\nin tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc(5150): 'status'\r\n Profiling failure on CUDNN engine 6#TC: UNKNOWN: CUDNN_STATUS_EXECUTION_FAILED\r\nin tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc(5150): 'status'\r\n Profiling failure on CUDNN engine 6: UNKNOWN: CUDNN_STATUS_EXECUTION_FAILED\r\nin tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc(5150): 'status'\r\n Profiling failure on CUDNN engine 5#TC: UNKNOWN: CUDNN_STATUS_INTERNAL_ERROR\r\nin tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc(5150): 'status'\r\n Profiling failure on CUDNN engine 5: UNKNOWN: CUDNN_STATUS_INTERNAL_ERROR\r\nin tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc(5150): 'status'\r\n Profiling failure on CUDNN engine 7#TC: UNKNOWN: CUDNN_STATUS_EXECUTION_FAILED\r\nin tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc(5150): 'status'\r\n Profiling failure on CUDNN engine 7: UNKNOWN: CUDNN_STATUS_EXECUTION_FAILED\r\nin tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc(5150): 'status'\r\n [[{{node model_1/conv2d_5/Relu}}]] [Op:__inference_train_function_1992]\r\n2023-07-24 18:29:40.809535: W tensorflow/core/kernels/data/generator_dataset_op.cc:108] Error occurred when finalizing GeneratorDataset iterator: FAILED_PRECONDITION: Python interpreter state is not initialized. The process may be terminated.\r\n [[{{node PyFunc}}]]", "> 2023-07-24 18:29:40.514447: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:221] Failure to initialize cublas may be due to OOM (cublas needs some free memory when you initialize it, and your deep-learning framework may have preallocated more than its fair share), or may be because this binary was not built with support for the GPU in your machine.\r\n\r\n@anand-shubham , Could you please refer to the above error and comment. This seems not an issue with tensorflow.", "WARNING:tensorflow:`period` argument is deprecated. Please use `save_freq` to specify the frequency in number of batches seen.\r\nEpoch 1/200\r\n2023-07-26 16:31:45.274459: I tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:428] Loaded cuDNN version 8100\r\n2023-07-26 16:33:46.507694: I tensorflow/tsl/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory\r\n2023-07-26 16:33:46.508174: I tensorflow/tsl/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory\r\n2023-07-26 16:33:46.508206: W tensorflow/compiler/xla/stream_executor/gpu/asm_compiler.cc:85] Couldn't get ptxas version string: INTERNAL: Couldn't invoke ptxas --version\r\n2023-07-26 16:33:46.508519: I tensorflow/tsl/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory\r\n2023-07-26 16:33:46.508591: W tensorflow/compiler/xla/stream_executor/gpu/redzone_allocator.cc:318] INTERNAL: Failed to launch ptxas\r\nRelying on driver to perform ptx compilation.\r\nModify $PATH to customize ptxas location.\r\nThis message will be only logged once.\r\n2023-07-26 16:38:08.228703: I tensorflow/compiler/xla/service/service.cc:173] XLA service 0x1e177620 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\r\n2023-07-26 16:38:08.228760: I tensorflow/compiler/xla/service/service.cc:181] StreamExecutor device (0): GRID V100L-8Q, Compute Capability 7.0\r\n2023-07-26 16:38:08.234554: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:268] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\r\n2023-07-26 16:38:08.256595: W tensorflow/compiler/xla/service/gpu/nvptx_helper.cc:56] Can't find libdevice directory ${CUDA_DIR}/nvvm/libdevice. This may result in compilation or runtime failures, if the program we try to run uses routines from libdevice.\r\nSearched for CUDA in the following directories:\r\n ./cuda_sdk_lib\r\n /usr/local/cuda-11.2\r\n /usr/local/cuda\r\n .\r\nYou can choose the search directory by setting xla_gpu_cuda_data_dir in HloModule's DebugOptions. For most apps, setting the environment variable XLA_FLAGS=--xla_gpu_cuda_data_dir=/path/to/cuda will work.\r\n2023-07-26 16:38:08.257645: W tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/gpu_backend_lib.cc:326] libdevice is required by this HLO module but was not found at ./libdevice.10.bc\r\n2023-07-26 16:38:08.257856: I tensorflow/compiler/jit/xla_compilation_cache.cc:477] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\r\n2023-07-26 16:38:08.257985: W tensorflow/core/framework/op_kernel.cc:1830] OP_REQUIRES failed at xla_ops.cc:446 : INTERNAL: libdevice not found at ./libdevice.10.bc\r\n2023-07-26 16:38:08.280213: W tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/gpu_backend_lib.cc:326] libdevice is required by this HLO module but was not found at ./libdevice.10.bc\r\n2023-07-26 16:38:08.280541: W tensorflow/core/framework/op_kernel.cc:1830] OP_REQUIRES failed at xla_ops.cc:446 : INTERNAL: libdevice not found at ./libdevice.10.bc\r\n2023-07-26 16:38:09.127454: W tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/gpu_backend_lib.cc:326] libdevice is required by this HLO module but was not found at ./libdevice.10.bc\r\n2023-07-26 16:38:09.127784: W tensorflow/core/framework/op_kernel.cc:1830] OP_REQUIRES failed at xla_ops.cc:446 : INTERNAL: libdevice not found at ./libdevice.10.bc\r\n2023-07-26 16:38:09.149194: W tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/gpu_backend_lib.cc:326] libdevice is required by this HLO module but was not found at ./libdevice.10.bc\r\n2023-07-26 16:38:09.149491: W tensorflow/core/framework/op_kernel.cc:1830] OP_REQUIRES failed at xla_ops.cc:446 : INTERNAL: libdevice not found at ./libdevice.10.bc\r\n2023-07-26 16:39:35.064225: W tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/gpu_backend_lib.cc:326] libdevice is required by this HLO module but was not found at ./libdevice.10.bc\r\n2023-07-26 16:39:35.064572: W tensorflow/core/framework/op_kernel.cc:1830] OP_REQUIRES failed at xla_ops.cc:446 : INTERNAL: libdevice not found at ./libdevice.10.bc\r\n2023-07-26 16:39:35.085963: W tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/gpu_backend_lib.cc:326] libdevice is required by this HLO module but was not found at ./libdevice.10.bc\r\n2023-07-26 16:39:35.086272: W tensorflow/core/framework/op_kernel.cc:1830] OP_REQUIRES failed at xla_ops.cc:446 : INTERNAL: libdevice not found at ./libdevice.10.bc\r\n2023-07-26 16:39:47.420330: W tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/gpu_backend_lib.cc:326] libdevice is required by this HLO module but was not found at ./libdevice.10.bc\r\n2023-07-26 16:39:47.420685: W tensorflow/core/framework/op_kernel.cc:1830] OP_REQUIRES failed at xla_ops.cc:446 : INTERNAL: libdevice not found at ./libdevice.10.bc\r\n2023-07-26 16:39:47.442196: W tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/gpu_backend_lib.cc:326] libdevice is required by this HLO module but was not found at ./libdevice.10.bc\r\n2023-07-26 16:39:47.442501: W tensorflow/core/framework/op_kernel.cc:1830] OP_REQUIRES failed at xla_ops.cc:446 : INTERNAL: libdevice not found at ./libdevice.10.bc\r\nTraceback (most recent call last):\r\n File \"/data01/PatternClassClassification/test_gpu1.py\", line 71, in <module>\r\n history = model.fit(\r\n File \"/data01/Software_installations/anaconda3/envs/gpu_env/lib/python3.10/site-packages/keras/utils/traceback_utils.py\", line 70, in error_handler\r\n raise e.with_traceback(filtered_tb) from None\r\n File \"/data01/Software_installations/anaconda3/envs/gpu_env/lib/python3.10/site-packages/tensorflow/python/eager/execute.py\", line 52, in quick_execute\r\n tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\r\ntensorflow.python.framework.errors_impl.InternalError: Graph execution error:\r\n\r\nDetected at node 'StatefulPartitionedCall_6' defined at (most recent call last):\r\n File \"/data01/PatternClassClassification/test_gpu1.py\", line 71, in <module>\r\n history = model.fit(\r\n File \"/data01/Software_installations/anaconda3/envs/gpu_env/lib/python3.10/site-packages/keras/utils/traceback_utils.py\", line 65, in error_handler\r\n return fn(*args, **kwargs)\r\n File \"/data01/Software_installations/anaconda3/envs/gpu_env/lib/python3.10/site-packages/keras/engine/training.py\", line 1650, in fit\r\n tmp_logs = self.train_function(iterator)\r\n File \"/data01/Software_installations/anaconda3/envs/gpu_env/lib/python3.10/site-packages/keras/engine/training.py\", line 1249, in train_function\r\n return step_function(self, iterator)\r\n File \"/data01/Software_installations/anaconda3/envs/gpu_env/lib/python3.10/site-packages/keras/engine/training.py\", line 1233, in step_function\r\n outputs = model.distribute_strategy.run(run_step, args=(data,))\r\n File \"/data01/Software_installations/anaconda3/envs/gpu_env/lib/python3.10/site-packages/keras/engine/training.py\", line 1222, in run_step\r\n outputs = model.train_step(data)\r\n File \"/data01/Software_installations/anaconda3/envs/gpu_env/lib/python3.10/site-packages/keras/engine/training.py\", line 1027, in train_step\r\n self.optimizer.minimize(loss, self.trainable_variables, tape=tape)\r\n File \"/data01/Software_installations/anaconda3/envs/gpu_env/lib/python3.10/site-packages/keras/optimizers/optimizer_experimental/optimizer.py\", line 527, in minimize\r\n self.apply_gradients(grads_and_vars)\r\n File \"/data01/Software_installations/anaconda3/envs/gpu_env/lib/python3.10/site-packages/keras/optimizers/optimizer_experimental/optimizer.py\", line 1140, in apply_gradients\r\n return super().apply_gradients(grads_and_vars, name=name)\r\n File \"/data01/Software_installations/anaconda3/envs/gpu_env/lib/python3.10/site-packages/keras/optimizers/optimizer_experimental/optimizer.py\", line 634, in apply_gradients\r\n iteration = self._internal_apply_gradients(grads_and_vars)\r\n File \"/data01/Software_installations/anaconda3/envs/gpu_env/lib/python3.10/site-packages/keras/optimizers/optimizer_experimental/optimizer.py\", line 1166, in _internal_apply_gradients\r\n return tf.__internal__.distribute.interim.maybe_merge_call(\r\n File \"/data01/Software_installations/anaconda3/envs/gpu_env/lib/python3.10/site-packages/keras/optimizers/optimizer_experimental/optimizer.py\", line 1216, in _distributed_apply_gradients_fn\r\n distribution.extended.update(\r\n File \"/data01/Software_installations/anaconda3/envs/gpu_env/lib/python3.10/site-packages/keras/optimizers/optimizer_experimental/optimizer.py\", line 1211, in apply_grad_to_update_var\r\n return self._update_step_xla(grad, var, id(self._var_key(var)))\r\nNode: 'StatefulPartitionedCall_6'\r\nlibdevice not found at ./libdevice.10.bc\r\n [[{{node StatefulPartitionedCall_6}}]] [Op:__inference_train_function_1141]\r\n2023-07-26 16:39:47.722164: W tensorflow/core/kernels/data/generator_dataset_op.cc:108] Error occurred when finalizing GeneratorDataset iterator: FAILED_PRECONDITION: Python interpreter state is not initialized. The process may be terminated.\r\n [[{{node PyFunc}}]]\r\n\r\n\r\n\r\nThe code runs with the CPU version of Tensorflow\r\n\r\nThe model is compiled and I can see the Architecture as the output, but when the training is about to start the program crashes", "Hi @anand-shubham ,\r\n\r\nSince you are using TF2.4v which is not supported anymore, can you please try with any of latest TF versions and let us know if issue still persists on latest versions ?\r\n\r\nAlso we officially supports Ubuntu instructions only which may work for other linux distros also. I can see from your inputs its not problem with Ubuntu right ? In that case it may not be possible to look into this issue unless this is a problem with latest TF versions also.\r\n\r\nPlease try with latest TF version and let us know the outcome. Thanks!", "hi @SuryanarayanaY ,\r\n\r\nThe above output is with TF2.11v", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61330\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61330\">No</a>\n", "@SuryanarayanaY after installing CUDA cuDNN by installing without conda the CNN has worked, but there is no speedup is that a Tensorflow issue?\r\n\r\nThe ETA for 1 epoch is much higher than what I get when running on CPU.", "Hi @anand-shubham ,\r\n\r\nCould you please submit a code snippet along with logs to test it on Ubuntu?\r\n\r\nThanks!\r\n\r\n", "Also it seems now issue is quite different than earlier.If earlier issue resolved and current problem is different it would be better to create a new ticket if you are ok with it.\r\n\r\nThanks!", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61330\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61330\">No</a>\n" ]

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[ "@TayyabaZainab0807 Could you please have a look at the post-training quantization. As per this [thread](https://discuss.tensorflow.org/t/quantizing-lstm-layers/1876) of TF forum unfortunately, Quantization aware training is not supports LSTM/RNN layers yet. It is in the process to be implemented. Thank you!\r\n\r\n", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further." ]

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[ "Hi @fcouziniedevy ,\r\n\r\nI'm unable to replicate your problem. The predictions using OMP_NUM_THREAD is constant and not random. Please refer the [gist](https://colab.research.google.com/gist/Varsha-anjanappa/ae9f5662513c39ace7c3a078e74d5bff/61328.ipynb).\r\n\r\nThank you!!", "Hi,\r\nsorry my post was not consistent: the problematic env variable is *OMP_THREAD_LIMIT* not *OMP_NUM_THREADS*. I used the correct variable everywhere in my post except in the example code... I edited my first post and by modifiing your example colab (replacing \"OMP_NUM_THREAD\" by \"OMP_THREAD_LIMIT\"), I can reproduce the problem." ]

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[ "@HAMZA12337,\r\nWe see that you are using tf version 1.15, 1.x is not actively supported, please update to 2.x and let us know if you are facing the same issue.\r\n\r\nAnd also you have provided the code which is suitable for 1.x version. Could you please try to migrate your TensorFlow code from TensorFlow 1.x to TensorFlow 2 with the help of this official document.\r\nhttps://www.tensorflow.org/guide/migrate\r\n\r\nAlso please try to install the tensorflow latest stable version by referring to the official build documentation.\r\nhttps://www.tensorflow.org/install\r\n\r\nThank you!", "But i don't now why i am using tensorflow 1.15 with gpu == geforce gtx 1070 it's work but with gpu rtx 4070 no", "@HAMZA12337,\r\n Its unlikely for TF 1.x version to receive any bug fixes. There is a high possibility that this was fixed with later TF versions. Perhaps you can use latest tf versions for your case. \r\nhttps://www.tensorflow.org/install\r\n\r\nCould you please try to migrate your TensorFlow code from TensorFlow 1.x to TensorFlow 2 with the help of this official document. Also **tf.compat.v1.Session** API was designed for TensorFlow v1. See the [TensorFlow v1 to TensorFlow v2 migration guide](https://www.tensorflow.org/guide/migrate) for instructions on how to migrate the rest of your code.\r\n\r\nThank you!", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61327\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61327\">No</a>\n" ]

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[ "Okay I fixed it! I needed to install another flavor of the libcudnn matching my cuda version:\r\n\r\n```\r\nsudo apt-get install libcudnn8=8.9.3.28-1+cuda11.8\r\n```", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61326\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61326\">No</a>\n" ]

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[ "@Pan17WJ Could you please have a look at this [doc](https://www.tensorflow.org/tfx/serving/tensorboard) on Tensor-board profiling and try with the latest TF version 2.13 ?Thank you!", "It doesn't seem to work. It generates the following file. When I opens the browser http://localhost:6006 by tensorboard, the display is\" No dashboards are active for the current data set. \"\r\n\r\n├── events.out.tfevents.1690428266.wj.profile-empty\r\n└── plugins\r\n └── profile\r\n └── 2023_07_27_11_24_26\r\n ├── wj.input_pipeline.pb\r\n ├── wj.kernel_stats.pb\r\n ├── wj.memory_profile.json.gz\r\n ├── wj.overview_page.pb\r\n ├── wj.tensorflow_stats.pb\r\n ├── wj.trace.json.gz\r\n └── wj.xplane.pb", "@Pan17WJ Is it failing for the latest TF v2.13 as well? If so please provide the complete standalone code to replicate this issue? Thank you!\r\n", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61325\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61325\">No</a>\n" ]

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[ "Hi @Armanasq,\r\n\r\nIn order to expedite the trouble-shooting process, please provide the complete code snippet to reproduce the issue reported here. I've tried to reproduce the issue [here](https://colab.research.google.com/gist/Varsha-anjanappa/55f59c3611c314f93550473980130af8/61324.ipynb), could you please take a look and let me know if i'm missing something . \r\n\r\nPlease find a similar issue [here](https://github.com/tensorflow/tensorflow/issues/34499) and let us know if it helps.\r\n\r\nThank you!!", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "Same issue", "Hi @YanSte ,\r\n\r\nPlease share colab link or simple standalone code with supporting files to reproduce the issue in our environment.It helps us in localizing the issue faster.\r\n\r\nThanks!", "Here are the projects:\r\n[Rice Classification](https://www.kaggle.com/code/yannicksteph/cnn-cv-rice-classification )\r\n[MNIST Classification](https://www.kaggle.com/code/yannicksteph/cnn-cv-mnist-classification)\r\n\r\nAlways at the first epoch, I have:\r\n`2023-08-09 13:10:29.937931: E tensorflow/core/grappler/optimizers/meta_optimizer.cc:954] layout failed: INVALID_ARGUMENT: Size of values 0 does not match size of permutation 4 @ fanin shape insequential_2/dropout/dropout/SelectV2-2-TransposeNHWCToNCHW-LayoutOptimizer`\r\n\r\nIt seems that adding a Dropout after a MaxPooling2D, Conv2D creates this problem.\r\n\r\n`TF version 1.7.0-rc1`\r\n\r\nDo you need more information?", "I got the same issue. \r\n[One notebook](https://www.kaggle.com/code/tsrdjan/second-try#Train) shows the issue, while [the other](https://www.kaggle.com/code/tsrdjan/kaggle-xy-h5-drugi-pokusaj#6.-Build-Deep-Learning-Model) does not.\r\n\r\nBoth of them are transfer learning. What is interesting to me is that I got this issue only when I try to use EfficientNets. At first, it was working fine (like it did in the second notebook, `kaggle-xy.h5-drugi-pokusaj`), but my newer attempts always have this issue. I tried to replicate architecture from 2nd notebook but it still doesn't remove the issue. So, as far as I can tell, this maybe has something to do with the input and processing part.\r\n\r\nThe 2nd notebook does have `Dropout` after pre-trained part, `GlobalAveragePooling2D` and `BatchNormalization` (in this order) and it was not an issue in my case.", "> I got the same issue. [One notebook](https://www.kaggle.com/code/tsrdjan/second-try#Train) shows the issue, while [the other](https://www.kaggle.com/code/tsrdjan/kaggle-xy-h5-drugi-pokusaj#6.-Build-Deep-Learning-Model) does not.\r\n> \r\n> Both of them are transfer learning. What is interesting to me is that I got this issue only when I try to use EfficientNets. At first, it was working fine (like it did in the second notebook, `kaggle-xy.h5-drugi-pokusaj`), but my newer attempts always have this issue. I tried to replicate architecture from 2nd notebook but it still doesn't remove the issue. So, as far as I can tell, this maybe has something to do with the input and processing part.\r\n> \r\n> The 2nd notebook does have `Dropout` after pre-trained part, `GlobalAveragePooling2D` and `BatchNormalization` (in this order) and it was not an issue in my case.\r\n\r\nUPDATE: I managed to fix the issue by recreating the architecture and adding `training=False`.\r\n\r\n**First version**:\r\n```Python\r\nimport tensorflow as tf\r\nfrom tensorflow.keras.applications.efficientnet_v2 import EfficientNetV2M\r\nfrom tensorflow.keras.models import Model\r\nfrom tensorflow.keras.layers import Dense, GlobalAveragePooling2D, BatchNormalization, Dropout\r\nfrom tensorflow.keras.optimizers import Adam\r\nfrom tensorflow.keras import regularizers\r\n\r\nbase_model = EfficientNetV2M(weights='imagenet', include_top=False, input_shape=(380, 380, 3), include_preprocessing=True)\r\n\r\nbase_model.trainable = False\r\n\r\ntop_dropout_rate = 0.2\r\nx = base_model.output\r\nx = GlobalAveragePooling2D()(x)\r\nx = Dense(256, activation='relu', kernel_regularizer=regularizers.l2(0.05))(x)\r\nx = BatchNormalization()(x)\r\nx = Dropout(top_dropout_rate, name=\"top_dropout\")(x)\r\nx = Dense(1, activation='sigmoid', name=\"prediction\")(x)\r\n\r\nmodel = tf.keras.Model(inputs=base_model.input, outputs=x)\r\n```\r\n\r\n**Second version**:\r\n```Python\r\nfrom tensorflow.keras.applications.efficientnet_v2 import EfficientNetV2M, preprocess_input\r\nfrom tensorflow.keras.models import Model\r\nfrom tensorflow.keras.layers import Dense, GlobalAveragePooling2D, BatchNormalization, Dropout, Input\r\nfrom tensorflow.keras.optimizers import Adam\r\nfrom tensorflow.keras import regularizers\r\n\r\nbase_model = EfficientNetV2M(weights='imagenet', include_top=False, input_shape=input_shape, include_preprocessing=True)\r\nbase_model.trainable = False\r\n\r\ntop_dropout_rate = 0.2\r\ninput_tensor = Input(shape=input_shape)\r\npreprocessed_input = preprocess_input(input_tensor)\r\n\r\nx = base_model(preprocessed_input, training=False)\r\nx = GlobalAveragePooling2D()(x)\r\nx = Dense(256, activation='relu', kernel_regularizer=regularizers.l2(0.05))(x)\r\nx = BatchNormalization()(x)\r\nx = Dropout(top_dropout_rate, name=\"top_dropout\")(x)\r\nx = Dense(1, activation='sigmoid', name=\"prediction\")(x)\r\n\r\nmodel = Model(inputs=input_tensor, outputs=x)\r\n```\r\n\r\nThe final version similar to 2nd, but with `x = base_model(preprocessed_input)` changed to `x = base_model(preprocessed_input, training=False)`. Adding this fixed the issue in second version.\r\n\r\nThe information for these changes was found in [keras's documentation for transfer learning](https://keras.io/guides/transfer_learning/).", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "Confirmed the error even after the modification by @TodorovicSrdjan (similar to version 2, using MobileNet V2 for transfer learning). Here the code:\r\n\r\n```python\r\n # Create the base model from the pre-trained MobileNet V2\r\n base_model = tf.keras.applications.MobileNetV2(input_shape=IMG_SHAPE,\r\n include_top=False,\r\n weights='imagenet')\r\n base_model.trainable = False\r\n data_augmentation = tf.keras.Sequential(\r\n [\r\n tf.keras.layers.RandomFlip(\"horizontal\"),\r\n tf.keras.layers.RandomRotation(0.2),\r\n ]\r\n )\r\n\r\n inputs = tf.keras.Input(shape=IMG_SHAPE)\r\n\r\n x = data_augmentation(inputs) # Apply random data augmentation\r\n x = tf.keras.applications.mobilenet_v2.preprocess_input(x)\r\n x = base_model(x, training=False)\r\n x = tf.keras.layers.Conv2D(filters=32, kernel_size=3, activation='relu')(x)\r\n x = tf.keras.layers.Dropout(0.2)(x)\r\n x = tf.keras.layers.GlobalAveragePooling2D()(x)\r\n x = tf.keras.layers.Dense(units=N_CLASSES, activation='softmax')(x)\r\n model = tf.keras.models.Model(inputs=inputs, outputs=x)\r\n``` \r\n\r\nAnd the output:\r\n\r\n```\r\nModel: \"model\"\r\n_________________________________________________________________\r\n Layer (type) Output Shape Param #\r\n=================================================================\r\n input_2 (InputLayer) [(None, 160, 160, 3)] 0\r\n\r\n sequential (Sequential) (None, 160, 160, 3) 0\r\n\r\n mobilenetv2_1.00_160 (Func (None, 5, 5, 1280) 2257984\r\n tional)\r\n\r\n conv2d (Conv2D) (None, 3, 3, 32) 368672\r\n\r\n dropout (Dropout) (None, 3, 3, 32) 0\r\n\r\n global_average_pooling2d ( (None, 32) 0\r\n GlobalAveragePooling2D)\r\n\r\n dense (Dense) (None, 7) 231\r\n\r\n=================================================================\r\nTotal params: 2626887 (10.02 MB)\r\nTrainable params: 368903 (1.41 MB)\r\nNon-trainable params: 2257984 (8.61 MB)\r\n_________________________________________________________________\r\nNumber of trainable weights = 4\r\nEpoch 1/3\r\n2023-08-21 12:39:38.540515: E tensorflow/core/grappler/optimizers/meta_optimizer.cc:954] layout failed: INVALID_ARGUMENT: Size of values 0 does not match size of permutation 4 @ fanin shape inmodel/dropout/dropout/SelectV2-2-TransposeNHWCToNCHW-LayoutOptimizer\r\n```\r\n\r\nTF 2.13.0 on cuDNN 8.6.0 and CUDA 12.2 on Linux x86_64.\r\n\r\n**Update: same code does not generate any error on MacBookPro M1 Pro with TF 2.13.0 and Metal backend**", "@Varsha-anjanappa This bug appears in many open issues, including [61687](https://github.com/tensorflow/tensorflow/issues/61687), has there been any progress on fixing the issue?", "@meekus-fischer I think it is related to adding dropout after the 2D CNN. after removing them the error has been fixed. However I am not sure. I decided to use PyTorch.", "Same issue here on TF 2.14 with a custom model, with layers like below as others have mentioned previously:\r\n\r\n```\r\ntf.keras.layers.Conv2D(64, 3, activation='relu'),\r\ntf.keras.layers.MaxPooling2D(),\r\ntf.keras.layers.Dropout(0.4),\r\n```\r\n\r\n", "I have similar/same error: \"2023-11-03 17:20:43.612803: E tensorflow/core/grappler/optimizers/meta_optimizer.cc:961] layout failed: INVALID_ARGUMENT: Size of values 0 does not match size of permutation 4 @ fanin shape insequential_10/dropout_31/dropout/SelectV2-2-TransposeNHWCToNCHW-LayoutOptimizer\"\r\n\r\nI can confirm it goes away when I comment out the dropout after the MaxPooling2D layers.\r\n\r\n```\r\n\t\tmodel.add(Conv2D(64, (3, 3), padding=\"same\"))\r\n\t\tmodel.add(Activation(\"relu\"))\r\n\t\tmodel.add(BatchNormalization(axis=chanDim))\r\n\t\tmodel.add(Conv2D(64, (3, 3), padding=\"same\"))\r\n\t\tmodel.add(Activation(\"relu\"))\r\n\t\tmodel.add(BatchNormalization(axis=chanDim))\r\n\t\tmodel.add(MaxPooling2D(pool_size=(2, 2)))\r\n\t\tmodel.add(Dropout(0.25)) <--- commenting out this line and the error goes away\r\n```", "See edumotya's response in #34499, as he suggests disabling the optimizer. This appears to have worked around the issue for me, although there's still something to be understood or resolved in the optimizer and/or Dropout's channel-first vs channel-last approach when the optimizer is enabled.", "> Confirmed the error even after the modification by @TodorovicSrdjan (similar to version 2, using MobileNet V2 for transfer learning). Here the code:\r\n> \r\n> ```python\r\n> # Create the base model from the pre-trained MobileNet V2\r\n> base_model = tf.keras.applications.MobileNetV2(input_shape=IMG_SHAPE,\r\n> include_top=False,\r\n> weights='imagenet')\r\n> base_model.trainable = False\r\n> data_augmentation = tf.keras.Sequential(\r\n> [\r\n> tf.keras.layers.RandomFlip(\"horizontal\"),\r\n> tf.keras.layers.RandomRotation(0.2),\r\n> ]\r\n> )\r\n> \r\n> inputs = tf.keras.Input(shape=IMG_SHAPE)\r\n> \r\n> x = data_augmentation(inputs) # Apply random data augmentation\r\n> x = tf.keras.applications.mobilenet_v2.preprocess_input(x)\r\n> x = base_model(x, training=False)\r\n> x = tf.keras.layers.Conv2D(filters=32, kernel_size=3, activation='relu')(x)\r\n> x = tf.keras.layers.Dropout(0.2)(x)\r\n> x = tf.keras.layers.GlobalAveragePooling2D()(x)\r\n> x = tf.keras.layers.Dense(units=N_CLASSES, activation='softmax')(x)\r\n> model = tf.keras.models.Model(inputs=inputs, outputs=x)\r\n> ```\r\n> \r\n> And the output:\r\n> \r\n> ```\r\n> Model: \"model\"\r\n> _________________________________________________________________\r\n> Layer (type) Output Shape Param #\r\n> =================================================================\r\n> input_2 (InputLayer) [(None, 160, 160, 3)] 0\r\n> \r\n> sequential (Sequential) (None, 160, 160, 3) 0\r\n> \r\n> mobilenetv2_1.00_160 (Func (None, 5, 5, 1280) 2257984\r\n> tional)\r\n> \r\n> conv2d (Conv2D) (None, 3, 3, 32) 368672\r\n> \r\n> dropout (Dropout) (None, 3, 3, 32) 0\r\n> \r\n> global_average_pooling2d ( (None, 32) 0\r\n> GlobalAveragePooling2D)\r\n> \r\n> dense (Dense) (None, 7) 231\r\n> \r\n> =================================================================\r\n> Total params: 2626887 (10.02 MB)\r\n> Trainable params: 368903 (1.41 MB)\r\n> Non-trainable params: 2257984 (8.61 MB)\r\n> _________________________________________________________________\r\n> Number of trainable weights = 4\r\n> Epoch 1/3\r\n> 2023-08-21 12:39:38.540515: E tensorflow/core/grappler/optimizers/meta_optimizer.cc:954] layout failed: INVALID_ARGUMENT: Size of values 0 does not match size of permutation 4 @ fanin shape inmodel/dropout/dropout/SelectV2-2-TransposeNHWCToNCHW-LayoutOptimizer\r\n> ```\r\n> \r\n> TF 2.13.0 on cuDNN 8.6.0 and CUDA 12.2 on Linux x86_64.\r\n> \r\n> **Update: same code does not generate any error on MacBookPro M1 Pro with TF 2.13.0 and Metal backend**\r\n\r\nHi @zzambia , I have tested your code and it seems working fine on colab though. Please check the [gist](https://colab.research.google.com/gist/SuryanarayanaY/7d76bd7471d3f17da6dedc5785d9c533/61324.ipynb) and confirm .\r\n\r\nHi @Armanasq , Could you please confirm whether this is still an issue. If so please submit a minimal code snippet to reproduce the issue. \r\n\r\nThanks!", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61324\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61324\">No</a>\n", "using `tf.nn.dropout(x, 0.5)` throws `E tensorflow/core/grappler/optimizers/meta_optimizer.cc:961] layout failed: INVALID_ARGUMENT: Size of values 0 does not match size of permutation 4 @ fanin shape inencoder_decoder_1/decoder_1/decoder_block_1/dropout/SelectV2-2-TransposeNHWCToNCHW-LayoutOptimizer`\r\n\r\nHowever, replacing it with `tf.keras.layers.Dropout(0.5)` resolved the issue for me. \r\n\r\nTF version: 2.17.0" ]

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[ "@willisacs-arm Did you try with the latest TF version 2.13 and let us know the outcome?\r\nHave a look at [this](https://www.tensorflow.org/lite/performance/post_training_quantization) reference for more information on quantization. Thank you!", "Hi yes the behaviour is the same for all versions above 2.12", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61323\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61323\">No</a>\n", "accidentally closed issue", "Hi @willisacs-arm \r\n\r\nI did observe that it does not add Quantize/Dequantize stubs for intermittent runs.\r\n\r\n@pkgoogle Could you please look into this issue?\r\n\r\nPlease find the reproducible [gist](https://colab.research.google.com/gist/pjpratik/bcd8711087797fe3874bf56073cb27d5/61323.ipynb) in TF 2.13.\r\n\r\nThanks.", "Hi @willisacs-arm, so currently the quantization is unable to quantize the unpack operator, so it actually processes that op in the original bit size. Would you like a feature request to make it quantizable? Are you working with a system that requires 8-bit operations Only?", "Hi, just to reiterate, the quantization sometimes works already if one just tries it enough times, so this doesn't seem to require a new feature, unless the fact that the quantization sometimes works is the bug here. It also works in patches before 2.12.0. \r\n\r\nYes I am working with a system that requires int 16 or int 8 activations. ", "Also if I understand it correctly the documentation says that the converter should throw an error if an operation cannot be quantized?\r\n\r\nhttps://www.tensorflow.org/lite/performance/post_training_quantization#integer_only", "Hi @willisacs-arm, yeah that documentation is incorrect, we have not finalized on intended behavior so we have not updated it to prevent further confusion. That being said, \"the quantization sometimes works already if one just tries it enough times\" doesn't sound good, is there any way you can provide us information on when it starts working? like... What is your model where this happens (especially which ops are included), and after how many times does it start working? If you can show us the converted state after each time, that'll also be great. Thanks!", "> Hi @willisacs-arm\r\n> \r\n> I did observe that it does not add Quantize/Dequantize stubs for intermittent runs.\r\n> \r\n> @pkgoogle Could you please look into this issue?\r\n> \r\n> Please find the reproducible [gist](https://colab.research.google.com/gist/pjpratik/bcd8711087797fe3874bf56073cb27d5/61323.ipynb) in TF 2.13.\r\n> \r\n> Thanks.\r\n\r\nAh alright, thanks. \r\n\r\nThe model is the one that was provided in the first comment in this issue, it has an unpack/unstack operator. It has also been provided neatly by pjpratik in the gist quoted in this reply. To reproduce the issue, run the notebook/gist and then repeat the last 2 steps until a quantized operator without dequantize/quantize ops appear in the Netron window. \r\n\r\nThere seems to just be a random chance every time one runs the converter to get a functioning quantized unstack operator. A speculation would be that there's a memory overwrite somewhere. The behaviour is present even when running the converter on a single core and while keeping the representative dataset constant. The model file to convert was also kept constant. ", "I was able to reproduce the somewhat random behavior after 7 attempts with the attached gist. @abattery, can you please take a look? Thanks.", "did this has solution? i got this promblem too" ]

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[ "Hi @akote123 , could you share the original verbose log, thanks!", "Hi @huiyan2021, I have uploaded here [vit_intel_log.zip](https://github.com/tensorflow/tensorflow/files/12105129/vit_intel_log.zip) \r\n", "I guess the reason is that _mklops are logged by <<, where the outputs are fully buffered if they are redirected to a file, while oneDNN verbose always flush stdout immediately: https://github.com/search?q=repo%3Aoneapi-src%2FoneDNN+fflush&type=code", "@huiyan2021 , one more observation what I found is some ops are common_runtime/eager/execute.cc and some are in common_runtime/executor.cc , here I am not able to understand why there is two path for execution ", "Hi @akote123,\r\n\r\nfrom the log I can see you are using xla, so for ops can not be jitted, they come to common_runtime/eager/execute.cc, otherwise they come to common_runtime/executor.cc\r\n\r\nalso, suggest that you use [trace viewer](https://www.tensorflow.org/guide/profiler#trace_viewer) to trace executions. ", "@huiyan2021 , do we have file location tensorflow source code where checking happen whether to go common_runtime/eager/execute.cc or the other one.\r\n", "@akote123 , you can refer to this article: https://whatdhack.medium.com/tensorflow-graph-graphdef-grappler-xla-mlir-llvm-etc-615191e96ebc, see XLA Flow part and call stack", "@huiyan2021 , In tensorflow the single model can go in both XLA and oneDNN or is it like either it will use XLA or oneDNN only\r\n", "Both. There may be different scenarios: \r\n1. Some parts of the model go in XLA path, some parts go in oneDNN path.\r\n2. Intel recently submitted a [pilot PR](https://github.com/tensorflow/tensorflow/pull/61237) to accelerate XLA’s Dot op with oneDNN.\r\n\r\nYou can refer to this RFC: https://docs.google.com/document/d/1ZzMcrjxITJeN2IjjgbzUjHh-4W1YgDUus3j25Dvn9ng/edit", "@huiyan2021 ,Thank you for the pointers I will got through it .\r\nActually for pretrained models how we can enable XLA both for inference and transfer learning", "same as training, you can refer to https://www.tensorflow.org/xla#enable_xla_for_tensorflow_models" ]

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[ "@stellaly As the error message indicates, please call invoke on the same thread where the GPU delegate is created as noted [here](https://www.tensorflow.org/lite/performance/gpu#android) and use the latest version of TFLiteGPUDelegate.\r\nThank you!", "> @stellaly As the error message indicates, please call invoke on the same thread where the GPU delegate is created as noted [here](https://www.tensorflow.org/lite/performance/gpu#android) and use the latest version of TFLiteGPUDelegate. Thank you!\r\n\r\nOf cource I call invoke on the same thread where the GPU delegate is created\r\nHere is my Android code\r\n```kotlin\r\n val modelloader = ModelLoader(context)\r\n \r\n val options = Interpreter.Options()\r\n options.addDelegate(GpuDelegate())\r\n// options.addDelegate(NnApiDelegate())\r\n\r\n val tflite = Interpreter(modelloader.loadMappedFile(\"model/tf_test.tflite\"), options)\r\n val inputShape0 = tflite.getInputTensorFromSignature(\"x\", \"compress_stage0\").shape()\r\n val inputShape1 = tflite.getInputTensorFromSignature(\"lambda_rd\", \"compress_stage0\").shape()\r\n \r\n val inputTensor0 = TensorBuffer.createFixedSize(\r\n inputShape0, DataType.FLOAT32\r\n )\r\n val inputTensor1 = TensorBuffer.createFixedSize(\r\n inputShape1, DataType.FLOAT32\r\n )\r\n\r\n val outputTensor = TensorBuffer.createFixedSize(\r\n tflite.getOutputTensorFromSignature(\"output_0\", \"compress_stage0\").shape(),\r\n DataType.FLOAT32\r\n )\r\n\r\n val inputsMap = mapOf(\"x\" to inputTensor0.buffer, \"lambda_rd\" to inputTensor1.buffer)\r\n val outputsMap = mapOf(\"output_0\" to outputTensor.buffer)\r\n tflite.runSignature(\r\n inputsMap,\r\n outputsMap,\r\n \"compress_stage0\"\r\n )\r\n```", "Hi @stellaly \r\n\r\nCan you please specify which version of TFLite you are using?\r\n\r\nFor Android 12 and above , please add the following lines in manifest file to detect GPU and let us know if it resolves the issue.\r\n\r\n```\r\n<uses-library android:name=\"libOpenCL.so\"\r\n android:required=\"false\"/>\r\n\r\n<uses-library android:name=\"libOpenCL-pixel.so\"\r\n android:required=\"false\"/>\r\n```\r\nThanks.", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further." ]

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[ "Did this show up in a performance profile or why did you decide to optimize this code?\r\n\r\nReadability is always subjective and one could argue that list comprehensions are easier to read.", "Hey @sherhut, Thanks for your feedback! The code optimization aimed to improve efficiency by consolidating loops. While I didn't run a performance profile, I believe it can enhance execution. Regarding readability, I'll consider list comprehensions. Open to other suggestions too :)", "Hey @sherhut,\r\nCould you please review it once you have a moment :)", "Hi @sherhut, Can you please review this PR ? Thank you!", "I agree this looks like a reasonable optimization on paper. But without seeing it improve something on a performance profile, it is hard to judge whether it is worth it. Readability is always subjective and I find both versions equally easy to read, if not preferring the original." ]

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[ "@sinban04 Could you please try to rerun the model using the latest TF version as you're using an older one?\r\nHave you gone through [this](https://www.tensorflow.org/lite/performance/post_training_quantization#full_integer_quantization) part already?\r\nThank you!", "@sushreebarsa \r\nSure, I already did try those in the link you gave. I wrote the code with the content in the link in the first place. \r\n\r\n> Could you please try to rerun the model using the latest TF version as you're using an older one?\r\n\r\nIn addition, I tried with the latest version as i said above (sorry, the content text is too long)\r\n\r\n> I tried other tensorflow versions (2.13.0, or nightly: 2.14.0-dev20230706) but still same as before.\r\n\r\n", "@sinban04 Thank you for the kind response!\r\n@pjpratik Could you please have a look at this?\r\nThank you!", "Hi @sinban04 \r\n\r\nI have tried to reproduce the issue with a toy model similar to the one provided and observed that model outputs different results when inputs change for full integer quantization and dynamic range quantization. Am I missing something here?\r\n\r\nPlease find the [gist](https://colab.research.google.com/gist/pjpratik/e969599feef00d0a7f00240a39caf0bb/61319.ipynb) and let us know if it helps.\r\n\r\nThanks.", "@pjpratik \r\nOkay, at first, thank you for your attention. \r\nI saw your gist code and even on your example, we can see the output is converging to a single value around 55~57.\r\nTo be clear, I wrote the code on the colab and the same problem is reproduced. \r\nPlz check this out: [mygist](https://colab.research.google.com/drive/1A-y90n8MLFqS71BGmPoZuEWIR7gBbB71?usp=sharing)\r\nYou would grasp the problem much better :)\r\n\r\nFYI, my original dataset is 501 line, and 24KB", "Hi @sinban04 \r\n\r\nThanks for the gist. \r\n\r\nI did observe similar output for the full integer quantization but for float32 input/output I did observe different outputs in your gist as well.\r\n>I saw your gist code and even on your example, we can see the output is converging to a single value around 55~57.\r\n\r\nYes, the reason is the quantization effect on the input/output. Please check this [Quantization scheme](https://www.tensorflow.org/lite/performance/quantization_spec) used by TFLite for 8 bit quantization.\r\n\r\n", "@pjpratik \r\n \r\n> but for float32 input/output I did observe different outputs in your gist as well.\r\n\r\nI tried as well after your comment. It's making different out for now, \r\nbut with all my dataset, it returns same floating value as i summarized the issue above\r\nAnd as you see, with integer only mode, it returns same output regardless of the input.\r\n\r\nFrom my experience, I assume that \r\nwhen the model is too complex (I suppose my model is not that complex though)\r\nduring quantization, the model weights are kinda quantized even not to function the proper prediction task.\r\n\r\nSo the simpler model you made returned some outputs (slightly different) but my model (more complex) did not.\r\nFor the same reason, with float16 quantization, \r\nwhen the model weights kinda \"burst\", it becomes impotent not to make proper predictions. \r\n(Y = Wx+b and it seems the weights are converging to 0, and the same output comes from bias)\r\n\r\nDo you think my theory is valid ? And there's no way to fix this problem for now ?\r\nFurthermore, i'm not sure what kind of model is appropriate for quantizaton and not (model compatibility for quantization)\r\nYou guys, Do you have any information about this quantization problem ?", "Hi @sinban04 \r\n\r\nThanks for the information.\r\n\r\nThe 8-bit quantization approximates floating point values using the following formula.\r\n\r\n$$real value = (int8 value - zero point) \\times scale$$\r\n\r\nAnd as per https://arxiv.org/abs/1712.05877, TFLite uses a single set of\r\nquantization parameters(scale and zero point)for all values within each activations array and within each weights array; separate arrays use separate quantization parameters.\r\n\r\nThe quantization scales are computed offline such that relative accuracy is maintained (Sec 2.2 of https://arxiv.org/abs/1712.05877), the Table 4.1 provides results of ResNet on ImageNet: Floating-point vs quantized network accuracy for various network depths.\r\n\r\nThanks.\r\n\r\n\r\n", "@pjpratik \r\nThank you for the paper reference :) \r\nI have one more question about my case. \r\nAs you can see in my first comment (main content at the top), i printed each layer of quantized model for debugging.\r\nI expected the values of weight are all in range between [-128, 127] or [0, 256] as it's quantized in 8bit. (unsigned, or signed)\r\nBut, some values out of range exist at some layer. \r\n\r\n```\r\n[ 2110 2151 -1352 1999 1340 -833 -1006 -657 -1454 -641 2383 2034\r\n 2358 -577 -1184 2455 2270 2831 -626 -1301 2957 2355 -665 -1340\r\n...\r\n -1140 2348 2233 2103 2280 2138 2450 2377 -1008 2026 1958 1892\r\n -1075 -1108 1946 -1078 2522 1899 1416 1882]\r\n```\r\nis it normal ? or is there some exceptional case ?\r\nOr i got something wrong ?\r\n", "Hi @sinban04 \r\n\r\nI see the weights are converted into int8 as intended and are in range [-128,127] when checked with netron.\r\n\r\nI have attached the weights and also the [gist](https://colab.research.google.com/gist/pjpratik/baa2c163df1bf564d126b69a815a7467/61319.ipynb).\r\n\r\n[FC3.txt](https://github.com/tensorflow/tensorflow/files/12247682/FC3.txt)\r\n[FC2.txt](https://github.com/tensorflow/tensorflow/files/12247683/FC2.txt)\r\n[FC1.txt](https://github.com/tensorflow/tensorflow/files/12247684/FC1.txt)\r\n\r\nThanks.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61319\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61319\">No</a>\n" ]

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[ "Hi @lutzroeder ,\r\n\r\nI have replicated the reported behaviour and attached logs below.\r\n\r\n```\r\n(bazel) suryanarayanay@surya-ubuntu20:~$ python\r\nPython 3.9.16 (main, Mar 8 2023, 14:00:05) \r\n[GCC 11.2.0] :: Anaconda, Inc. on linux\r\nType \"help\", \"copyright\", \"credits\" or \"license\" for more information.\r\n>>> import os\r\n>>> os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\r\n>>> import tensorflow\r\nWARNING:tensorflow:From /home/suryanarayanay/miniconda3/envs/bazel/lib/python3.9/site-packages/tensorflow/python/ops/distributions/distribution.py:259: ReparameterizationType.__init__ (from tensorflow.python.ops.distributions.distribution) is deprecated and will be removed after 2019-01-01.\r\nInstructions for updating:\r\nThe TensorFlow Distributions library has moved to TensorFlow Probability (https://github.com/tensorflow/probability). You should update all references to use `tfp.distributions` instead of `tf.distributions`.\r\nWARNING:tensorflow:From /home/suryanarayanay/miniconda3/envs/bazel/lib/python3.9/site-packages/tensorflow/python/ops/distributions/bernoulli.py:165: RegisterKL.__init__ (from tensorflow.python.ops.distributions.kullback_leibler) is deprecated and will be removed after 2019-01-01.\r\nInstructions for updating:\r\nThe TensorFlow Distributions library has moved to TensorFlow Probability (https://github.com/tensorflow/probability). You should update all references to use `tfp.distributions` instead of `tf.distributions`.\r\n```", "@lutzroeder ,\r\n\r\nPlease feel free to raise a pull request if you are willing to contribute. Thanks!", "These are deprecation warnings to make sure you update to APIs which won't get deleted on the next release. It is not an issue.", "@mihaimaruseac the output shows when running `import tensorflow` and clutters logs even if none of the APIs mentioned are used. The default method to disable this unnecessary log output does not work. That is an issue.", "Hi, \r\n\r\nYou can silence the command line output temporarily while importing the library and then enable it back again.\r\nBelow is the code which silences the warning from tensorflow library.\r\n\r\n```\r\nimport os\r\nimport sys\r\n\r\n\r\n# silence command-line output temporarily\r\nsys.stdout, sys.stderr = os.devnull, os.devnull\r\n\r\nimport tensorflow as tf\r\n\r\n# unsilence command-line output\r\nsys.stdout, sys.stderr = sys.__stdout__, sys.__stderr__\r\n```\r\n", "Running this before `import tensorflow` worked when building from main, turning off log output is not the right answer.\r\n```\r\nimport logging\r\nlogging.getLogger('tensorflow').setLevel(logging.ERROR)\r\n```\r\nIs it possible to fix the deprecation warning to only show if these types are actually used? Why is the warning now showing for all users of TensorFlow.", "@lutzroeder this does not work for me:\r\n\r\n\r\n\r\nI am using TensorFlow 2.14.0rc0.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61318\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61318\">No</a>\n", "@salcc this is for @mihaimaruseac to investigate. Closing issue given no follow up from maintainers. " ]

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[ "@maciejskorski I tried to replicate the issue on colab using TF v2.12, please find the attached [gist](https://colab.research.google.com/gist/sushreebarsa/a371a4c340eabea40555a380de1bf2b2/61317.ipynb) here. I have got the output as ; `1889`. Could you please confirm the same?\r\nThank you!", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61317\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61317\">No</a>\n", "@SuryanarayanaY This issue is replicating [here](https://colab.sandbox.google.com/gist/SuryanarayanaY/e5973644e0b87a3cda3680a24b5956ac/copy-of-61317.ipynb) in the colab gist. Thank you!", "@sushreebarsa In your case the snaphost was created twice in different dirs, right?\r\nKindly note that I reported the issue under `tensorflow==2.11` while as of now Colab has `2.13` (let us not rely on Colab exclusively or track the version).", "Hi @maciejskorski ,\r\n\r\nI have tested with TF2.13v and observed different fingerprints. Also the fingerprints count changes with `batch_size` and increases with no of runs. Each fingerprint have Two shard files and one metadata file. Attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/d69956adb47cb0355d00d483fde98a02/61317_2-13v.ipynb) for reference.\r\n\r\nWill escalate to concern SME and come back." ]

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[ "Hi @vinayk114 \r\n\r\nThere is numpy dependency, please check if you've installed correct numpy version .\r\nhttps://github.com/tensorflow/tensorflow/blob/0db597d0d758aba578783b5bf46c889700a45085/tensorflow/tools/pip_package/setup.py#L97\r\nYou can install numpy by running **pip install numpy==1.22**. The compatible version for tensorflow v2.12 is numpy >= 1.22, <1.24.\r\nPlease let us know if that helps?\r\n\r\nThank you!!", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61316\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61316\">No</a>\n" ]

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[ "From the template it looks like you are installing **TensorFlow** (TF) prebuilt binaries:\n * For TF-GPU - See point 1\n * For TF-CPU - See point 2\n-----------------------------------------------------------------------------------------------\n**1. Installing **TensorFlow-GPU** (TF) prebuilt binaries**\n\nMake sure you are using compatible TF and CUDA versions. Please refer following TF version and CUDA version compatibility table.\n| TF | CUDA |\n| :-------------: | :-------------: |\n| 2.5.0 | 11.2 |\n| 2.4.0 | 11.0 |\n| 2.1.0 - 2.3.0 | 10.1 |\n| 1.13.1 - 2.0 | 10.0 |\n| 1.5.0 - 1.12.0 | 9.0 |\n\n * If you have above configuration and using _**Windows**_ platform -\n * Try adding the CUDA, CUPTI, and cuDNN installation directories to the %PATH% environment variable.\n * Refer [windows setup guide](https://www.tensorflow.org/install/gpu#windows_setup).\n * If you have above configuration and using _**Ubuntu/Linux**_ platform -\n * Try adding the CUDA, CUPTI, and cuDNN installation directories to the $LD_LIBRARY_PATH environment variable.\n * Refer [linux setup guide](https://www.tensorflow.org/install/gpu#linux_setup).\n * If error still persists then, apparently your CPU model does not support AVX instruction sets.\n * Refer [hardware requirements](https://www.tensorflow.org/install/pip#hardware-requirements).\n\n-----------------------------------------------------------------------------------------------\n**2. Installing **TensorFlow** (TF) CPU prebuilt binaries**\n\n*TensorFlow release binaries version 1.6 and higher are prebuilt with AVX instruction sets.*\n\nTherefore on any CPU that does not have these instruction sets, either CPU or GPU version of TF will fail to load.\nApparently, your CPU model does not support AVX instruction sets. You can still use TensorFlow with the alternatives given below:\n\n * Try Google Colab to use TensorFlow.\n * The easiest way to use TF will be to switch to [google colab](https://colab.sandbox.google.com/notebooks/welcome.ipynb#recent=true). You get pre-installed latest stable TF version. Also you can use ```pip install``` to install any other preferred TF version.\n * It has an added advantage since you can you easily switch to different hardware accelerators (cpu, gpu, tpu) as per the task.\n * All you need is a good internet connection and you are all set.\n * Try to build TF from sources by changing CPU optimization flags.\n\n*Please let us know if this helps.*\n", "@helmunt1998 You are using an older version of TF which is not actively supported. Could you upgrade to the latest TF version and follow this migration [document](https://www.tensorflow.org/guide/migrate) to know more about this. The older version might show these issues as many apis are being deprecated. Please let us know if that help?\r\nThank you!", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61315\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61315\">No</a>\n" ]

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[ "I have the same issue\r\nsource binary\r\ntensorflow 2.13.0\r\nPython 3.9.1\r\nCUDA 12.1.1-1\r\nCUDnn 8.9.1.23\r\nGPU 3x NVIDIA V100", "Hi @nikita-savelyevv ,\r\n\r\nFrom your attached code snippet i have changed this line:\r\n`train_dataset = mnist_test.cache().shuffle(10000).batch(batch_size)\r\n`\r\n to :\r\n`train_dataset = mnist_train.cache().shuffle(10000).batch(batch_size)\r\n`\r\nand then executed the code on a GCP VM with 4 GPUs. The logs are attached below.\r\n\r\n```\r\n(bazel) suryanarayanay@surya-ubuntu20:~$ python 61314_r3.py\r\n2023-07-19 08:52:48.833519: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:8893] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\r\n2023-07-19 08:52:48.833729: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\r\n2023-07-19 08:52:48.837974: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\r\n2023-07-19 08:52:49.192706: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\nTo enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2023-07-19 08:52:50.862937: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\r\nWARNING:tensorflow:From /home/suryanarayanay/miniconda3/envs/bazel/lib/python3.9/site-packages/tensorflow/python/ops/distributions/distribution.py:259: ReparameterizationType.__init__ (from tensorflow.python.ops.distributions.distribution) is deprecated and will be removed after 2019-01-01.\r\nInstructions for updating:\r\nThe TensorFlow Distributions library has moved to TensorFlow Probability (https://github.com/tensorflow/probability). You should update all references to use `tfp.distributions` instead of `tf.distributions`.\r\nWARNING:tensorflow:From /home/suryanarayanay/miniconda3/envs/bazel/lib/python3.9/site-packages/tensorflow/python/ops/distributions/bernoulli.py:165: RegisterKL.__init__ (from tensorflow.python.ops.distributions.kullback_leibler) is deprecated and will be removed after 2019-01-01.\r\nInstructions for updating:\r\nThe TensorFlow Distributions library has moved to TensorFlow Probability (https://github.com/tensorflow/probability). You should update all references to use `tfp.distributions` instead of `tf.distributions`.\r\n2023-07-19 08:53:01.959096: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-07-19 08:53:01.961067: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-07-19 08:53:01.962833: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-07-19 08:53:01.964631: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-07-19 08:53:02.295809: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-07-19 08:53:02.297743: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-07-19 08:53:02.299515: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-07-19 08:53:02.301298: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-07-19 08:53:02.303146: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-07-19 08:53:02.304774: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-07-19 08:53:02.306338: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-07-19 08:53:02.307986: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-07-19 08:53:03.493607: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-07-19 08:53:03.495717: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-07-19 08:53:03.497548: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-07-19 08:53:03.499358: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-07-19 08:53:03.501174: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-07-19 08:53:03.502817: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-07-19 08:53:03.504433: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-07-19 08:53:03.506064: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-07-19 08:53:03.507816: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-07-19 08:53:03.509473: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-07-19 08:53:03.511157: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-07-19 08:53:03.512805: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-07-19 08:53:06.737342: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-07-19 08:53:06.739414: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-07-19 08:53:06.741220: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-07-19 08:53:06.743084: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-07-19 08:53:06.744986: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-07-19 08:53:06.746639: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-07-19 08:53:06.748217: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-07-19 08:53:06.749874: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-07-19 08:53:06.751576: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-07-19 08:53:06.753158: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1831] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13623 MB memory: -> device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5\r\n2023-07-19 08:53:06.753544: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-07-19 08:53:06.755104: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1831] Created device /job:localhost/replica:0/task:0/device:GPU:1 with 13623 MB memory: -> device: 1, name: Tesla T4, pci bus id: 0000:00:05.0, compute capability: 7.5\r\n2023-07-19 08:53:06.755482: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-07-19 08:53:06.757076: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1831] Created device /job:localhost/replica:0/task:0/device:GPU:2 with 13623 MB memory: -> device: 2, name: Tesla T4, pci bus id: 0000:00:06.0, compute capability: 7.5\r\n2023-07-19 08:53:06.757437: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-07-19 08:53:06.759072: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1831] Created device /job:localhost/replica:0/task:0/device:GPU:3 with 13623 MB memory: -> device: 3, name: Tesla T4, pci bus id: 0000:00:07.0, compute capability: 7.5\r\nDevices: 1\r\n2023-07-19 08:53:07.299239: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:552] The `assert_cardinality` transformation is currently not handled by the auto-shard rewrite and will be removed.\r\n2023-07-19 08:53:10.842723: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x3a569dd0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\r\n2023-07-19 08:53:10.842919: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Tesla T4, Compute Capability 7.5\r\n2023-07-19 08:53:10.842983: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (1): Tesla T4, Compute Capability 7.5\r\n2023-07-19 08:53:10.843030: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (2): Tesla T4, Compute Capability 7.5\r\n2023-07-19 08:53:10.843085: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (3): Tesla T4, Compute Capability 7.5\r\n2023-07-19 08:53:10.944601: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\r\n2023-07-19 08:53:11.479846: I tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:440] Loaded cuDNN version 8600\r\n2023-07-19 08:53:11.729654: I tensorflow/tsl/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory\r\n2023-07-19 08:53:11.981977: I ./tensorflow/compiler/jit/device_compiler.h:186] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\r\n938/938 [==============================] - 10s 6ms/step - loss: 10.1223 - accuracy: 0.8286 \r\nDevices: 2\r\n2023-07-19 08:53:19.515161: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:552] The `assert_cardinality` transformation is currently not handled by the auto-shard rewrite and will be removed.\r\n2023-07-19 08:53:47.328426: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:422] Filling up shuffle buffer (this may take a while): 2 of 10000\r\n2023-07-19 08:53:52.679706: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:422] Filling up shuffle buffer (this may take a while): 3 of 10000\r\n2023-07-19 08:54:08.411572: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:422] Filling up shuffle buffer (this may take a while): 5 of 10000\r\n2023-07-19 08:54:20.882652: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:422] Filling up shuffle buffer (this may take a while): 6 of 10000\r\n2023-07-19 08:54:34.713750: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:422] Filling up shuffle buffer (this may take a while): 7 of 10000\r\n2023-07-19 08:54:54.214257: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:422] Filling up shuffle buffer (this may take a while): 8 of 10000\r\n2023-07-19 08:55:09.061208: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:422] Filling up shuffle buffer (this may take a while): 9 of 10000\r\n2023-07-19 08:55:14.850822: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:422] Filling up shuffle buffer (this may take a while): 10 of 10000\r\n2023-07-19 08:55:20.150592: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:422] Filling up shuffle buffer (this may take a while): 11 of 10000\r\n2023-07-19 08:55:25.772313: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:422] Filling up shuffle buffer (this may take a while): 12 of 10000\r\n2023-07-19 08:55:31.710363: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:422] Filling up shuffle buffer (this may take a while): 13 of 10000\r\n2023-07-19 08:55:38.318281: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:422] Filling up shuffle buffer (this may take a while): 14 of 10000\r\n2023-07-19 08:55:44.011665: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:422] Filling up shuffle buffer (this may take a while): 15 of 10000\r\n2023-07-19 08:55:54.076764: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:422] Filling up shuffle buffer (this may take a while): 17 of 10000\r\n2023-07-19 08:56:07.181624: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:422] Filling up shuffle buffer (this may take a while): 19 of 10000\r\n2023-07-19 08:56:14.434589: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:422] Filling up shuffle buffer (this may take a while): 20 of 10000\r\n2023-07-19 08:56:29.020860: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:422] Filling up shuffle buffer (this may take a while): 22 of 10000\r\n2023-07-19 08:56:35.686768: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:422] Filling up shuffle buffer (this may take a while): 23 of 10000\r\n2023-07-19 08:56:47.350548: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:422] Filling up shuffle buffer (this may take a while): 25 of 10000\r\n2023-07-19 08:56:53.206492: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:422] Filling up shuffle buffer (this may take a while): 26 of 10000\r\n2023-07-19 08:57:03.189459: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:422] Filling up shuffle buffer (this may take a while): 28 of 10000\r\n2023-07-19 08:57:13.485880: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:422] Filling up shuffle buffer (this may take a while): 30 of 10000\r\n2023-07-19 08:57:24.014117: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:422] Filling up shuffle buffer (this may take a while): 32 of 10000\r\n2023-07-19 08:57:38.654104: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:422] Filling up shuffle buffer (this may take a while): 34 of 10000\r\n2023-07-19 08:57:45.836962: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:422] Filling up shuffle buffer (this may take a while): 35 of 10000\r\n2023-07-19 08:57:53.215866: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:422] Filling up shuffle buffer (this may take a while): 36 of 10000\r\n2023-07-19 08:58:03.464771: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:422] Filling up shuffle buffer (this may take a while): 38 of 10000\r\n2023-07-19 08:58:18.851690: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:422] Filling up shuffle buffer (this may take a while): 39 of 10000\r\n2023-07-19 08:58:37.214024: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:422] Filling up shuffle buffer (this may take a while): 40 of 10000\r\n2023-07-19 08:58:55.288479: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:422] Filling up shuffle buffer (this may take a while): 41 of 10000\r\n2023-07-19 08:59:06.636724: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:422] Filling up shuffle buffer (this may take a while): 42 of 10000\r\n2023-07-19 08:59:21.521155: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:422] Filling up shuffle buffer (this may take a while): 43 of 10000\r\n2023-07-19 08:59:34.989589: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:422] Filling up shuffle buffer (this may take a while): 44 of 10000\r\n2023-07-19 08:59:48.707579: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:422] Filling up shuffle buffer (this may take a while): 45 of 10000\r\n2023-07-19 09:00:05.466870: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:422] Filling up shuffle buffer (this may take a while): 46 of 10000\r\n2023-07-19 09:00:22.107982: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:422] Filling up shuffle buffer (this may take a while): 47 of 10000\r\n2023-07-19 09:00:37.668419: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:422] Filling up shuffle buffer (this may take a while): 48 of 10000\r\n2023-07-19 09:00:51.022583: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:422] Filling up shuffle buffer (this may take a while): 49 of 10000\r\n2023-07-19 09:01:04.442588: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:422] Filling up shuffle buffer (this may take a while): 50 of 10000\r\n2023-07-19 09:01:41.646110: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:422] Filling up shuffle buffer (this may take a while): 51 of 10000\r\n2023-07-19 09:02:27.555847: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:422] Filling up shuffle buffer (this may take a while): 52 of 10000\r\n2023-07-19 09:03:03.864518: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:422] Filling up shuffle buffer (this may take a while): 53 of 10000\r\n\r\n\r\n```\r\n\r\nWhen `devices=1` the code executes fine for me. When `devices>=2` for filling shuffle buffer its taking very much time. This should not be the case.There seems some problem wrt performance but not sure whether your reported behaviour able to replicable or not since I have stopped as the code taking too much time. The code used is attached as [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/e631267c482a7cc91df962f49892351f/61314_r3.ipynb) here.", "I have also tested the same code snippet attached by @nikita-savelyevv and found program hangs when devices=2 started executing. Logas attached below.\r\n\r\n```\r\n(bazel) suryanarayanay@surya-ubuntu20:~$ python 61314_r2.py\r\n2023-07-19 09:15:25.741329: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:8893] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\r\n2023-07-19 09:15:25.741552: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\r\n2023-07-19 09:15:25.746410: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\r\n2023-07-19 09:15:26.157582: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\nTo enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2023-07-19 09:15:27.903770: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\r\nWARNING:tensorflow:From /home/suryanarayanay/miniconda3/envs/bazel/lib/python3.9/site-packages/tensorflow/python/ops/distributions/distribution.py:259: ReparameterizationType.__init__ (from tensorflow.python.ops.distributions.distribution) is deprecated and will be removed after 2019-01-01.\r\nInstructions for updating:\r\nThe TensorFlow Distributions library has moved to TensorFlow Probability (https://github.com/tensorflow/probability). You should update all references to use `tfp.distributions` instead of `tf.distributions`.\r\nWARNING:tensorflow:From /home/suryanarayanay/miniconda3/envs/bazel/lib/python3.9/site-packages/tensorflow/python/ops/distributions/bernoulli.py:165: RegisterKL.__init__ (from tensorflow.python.ops.distributions.kullback_leibler) is deprecated and will be removed after 2019-01-01.\r\nInstructions for updating:\r\nThe TensorFlow Distributions library has moved to TensorFlow Probability (https://github.com/tensorflow/probability). You should update all references to use `tfp.distributions` instead of `tf.distributions`.\r\n2023-07-19 09:15:38.960161: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-07-19 09:15:38.962045: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-07-19 09:15:38.963767: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-07-19 09:15:38.965520: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-07-19 09:15:39.264719: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-07-19 09:15:39.266613: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-07-19 09:15:39.268353: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-07-19 09:15:39.270109: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-07-19 09:15:39.271916: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-07-19 09:15:39.273529: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-07-19 09:15:39.275073: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-07-19 09:15:39.276628: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-07-19 09:15:40.428372: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-07-19 09:15:40.430244: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-07-19 09:15:40.432022: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-07-19 09:15:40.433705: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-07-19 09:15:40.435508: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-07-19 09:15:40.437049: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-07-19 09:15:40.438592: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-07-19 09:15:40.440161: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-07-19 09:15:40.441733: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-07-19 09:15:40.443276: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-07-19 09:15:40.444815: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-07-19 09:15:40.446383: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-07-19 09:15:43.793004: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-07-19 09:15:43.794994: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-07-19 09:15:43.796831: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-07-19 09:15:43.798663: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-07-19 09:15:43.800458: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-07-19 09:15:43.802054: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-07-19 09:15:43.803632: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-07-19 09:15:43.805221: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-07-19 09:15:43.806810: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-07-19 09:15:43.808346: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1831] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13623 MB memory: -> device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5\r\n2023-07-19 09:15:43.808735: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-07-19 09:15:43.810249: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1831] Created device /job:localhost/replica:0/task:0/device:GPU:1 with 13623 MB memory: -> device: 1, name: Tesla T4, pci bus id: 0000:00:05.0, compute capability: 7.5\r\n2023-07-19 09:15:43.810625: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-07-19 09:15:43.812202: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1831] Created device /job:localhost/replica:0/task:0/device:GPU:2 with 13623 MB memory: -> device: 2, name: Tesla T4, pci bus id: 0000:00:06.0, compute capability: 7.5\r\n2023-07-19 09:15:43.812576: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-07-19 09:15:43.814108: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1831] Created device /job:localhost/replica:0/task:0/device:GPU:3 with 13623 MB memory: -> device: 3, name: Tesla T4, pci bus id: 0000:00:07.0, compute capability: 7.5\r\nDevices: 1\r\n2023-07-19 09:15:44.326961: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:552] The `assert_cardinality` transformation is currently not handled by the auto-shard rewrite and will be removed.\r\n2023-07-19 09:15:47.663646: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x3a7b33e0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\r\n2023-07-19 09:15:47.663778: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Tesla T4, Compute Capability 7.5\r\n2023-07-19 09:15:47.663798: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (1): Tesla T4, Compute Capability 7.5\r\n2023-07-19 09:15:47.663810: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (2): Tesla T4, Compute Capability 7.5\r\n2023-07-19 09:15:47.663821: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (3): Tesla T4, Compute Capability 7.5\r\n2023-07-19 09:15:47.778929: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\r\n2023-07-19 09:15:48.326253: I tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:440] Loaded cuDNN version 8600\r\n2023-07-19 09:15:48.567179: I tensorflow/tsl/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory\r\n2023-07-19 09:15:48.771324: I ./tensorflow/compiler/jit/device_compiler.h:186] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\r\n157/157 [==============================] - 5s 2ms/step - loss: 26.1179 - accuracy: 0.6900\r\nDevices: 2\r\n2023-07-19 09:15:50.748191: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:552] The `assert_cardinality` transformation is currently not handled by the auto-shard rewrite and will be removed.\r\n##Hangs here\r\n```\r\n\r\nTested code attached here as colab [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/94b25afc6c081078a6a7446ee5020db4/61314_r2.ipynb).\r\n\r\nThanks!", "@SuryanarayanaY Thanks for reaching out! I used `mnist_test` intentionally to slightly speed up the reproduction.\r\n\r\nI agree with your results. For me, when order of devices is set to `1, 2, 3`, the case `devices=2` also hangs as you describe. For the order `1, 3, 2`, the case `devices=2` produces the error I've attached in the ticket.\r\n\r\nSince the machine you run the code on has 4 GPUs, I would suppose that setting the order to something like `1, 4, 3, 2` would also lead to the error I attached.\r\n\r\nAnyway, I would assume that these two problems (hanging and throwing error) are related and may have the same cause.", "Adding to distributed training hanging with `tensorflow==2.13.1`\r\n\r\nSmall fashion mnist example to reproduce jit_compiled model fails to train and hangs:\r\n\r\n```\r\nimport tensorflow as tf\r\nfrom keras import Model\r\nfrom keras.layers import Dense, Dropout, Flatten, Input\r\nfrom keras.utils import set_random_seed\r\n\r\n\r\ndef get_model() -> Model:\r\n set_random_seed(42)\r\n inp = Input(shape=(28, 28))\r\n inp = tf.expand_dims(inp, axis=-1)\r\n flt = Flatten()(inp)\r\n hdn = Dense(32, activation=\"relu\")(flt)\r\n drp = Dropout(0.2)(hdn)\r\n out = Dense(10)(drp)\r\n model = Model(inputs=inp, outputs=out, name=\"mnist_model\")\r\n print(model.summary())\r\n\r\n return model\r\n\r\n\r\ndef main():\r\n strategy = tf.distribute.MirroredStrategy()\r\n print(\"Number of devices: {}\".format(strategy.num_replicas_in_sync))\r\n assert (\r\n strategy.num_replicas_in_sync > 1\r\n ), \"strategy.num_replicas_in_sync must be greater than 1 or else problem will not be shown\"\r\n with strategy.scope():\r\n model = get_model()\r\n model.compile(\r\n optimizer=\"adam\",\r\n loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\r\n metrics=[\"accuracy\"],\r\n jit_compile=True, # FIXME: jit compiled model will fail to hang during fit\r\n )\r\n\r\n fashion_mnist = tf.keras.datasets.fashion_mnist\r\n (train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()\r\n\r\n train_images = train_images / 255.0\r\n test_images = test_images / 255.0\r\n model.fit(train_images, train_labels, epochs=5, batch_size=1024)\r\n\r\n\r\nif __name__ == \"__main__\":\r\n main()\r\n```\r\n\r\n```\r\n2023-07-25 09:31:22.920814: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-07-25 09:31:22.922395: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1639] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13576 MB memory: -> device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5\r\n2023-07-25 09:31:22.923073: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-07-25 09:31:22.924679: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1639] Created device /job:localhost/replica:0/task:0/device:GPU:1 with 13576 MB memory: -> device: 1, name: Tesla T4, pci bus id: 0000:00:05.0, compute capability: 7.5\r\nNumber of devices: 2\r\nModel: \"mnist_model\"\r\n_________________________________________________________________\r\n Layer (type) Output Shape Param # \r\n=================================================================\r\n input_2 (InputLayer) [(None, 28, 28, 1)] 0 \r\n \r\n flatten (Flatten) (None, 784) 0 \r\n \r\n dense (Dense) (None, 32) 25120 \r\n \r\n dropout (Dropout) (None, 32) 0 \r\n \r\n dense_1 (Dense) (None, 10) 330 \r\n \r\n=================================================================\r\nTotal params: 25450 (99.41 KB)\r\nTrainable params: 25450 (99.41 KB)\r\nNon-trainable params: 0 (0.00 Byte)\r\n_________________________________________________________________\r\nNone\r\nEpoch 1/5\r\n2023-07-25 09:31:25.593085: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x296a66b0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\r\n2023-07-25 09:31:25.593171: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Tesla T4, Compute Capability 7.5\r\n2023-07-25 09:31:25.593209: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (1): Tesla T4, Compute Capability 7.5\r\n2023-07-25 09:31:25.641984: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:255] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\r\n2023-07-25 09:31:25.662430: W tensorflow/compiler/tf2xla/kernels/random_ops.cc:57] Warning: Using tf.random.uniform with XLA compilation will ignore seeds; consider using tf.random.stateless_uniform instead if reproducible behavior is desired. mnist_model/dropout/dropout/random_uniform/RandomUniform\r\n2023-07-25 09:31:26.958144: I tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:432] Loaded cuDNN version 8600\r\n2023-07-25 09:31:26.984048: I tensorflow/tsl/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory\r\n2023-07-25 09:31:27.345626: I tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:432] Loaded cuDNN version 8600\r\n2023-07-25 09:31:28.252749: I ./tensorflow/compiler/jit/device_compiler.h:186] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\r\n......hang here and no more log lines\r\n````", "Same issue here! Using 4GPU for distributed training on Ubuntu 22.04 with Tensorflow 2.13 hangs at the “compiled cluster using the XLA” line. Issue solved by downgrading to 2.12 ", "Same for me with RTX 8000 and A6000 setup in MirrorStrategy with NCCL and Hierarchical CrossDeviceOps. I get a huge block of `tensorflow/core/framework/local_rendezvous.cc:405 Local rendezvous recv item cancelled.` before first epoch, but it doesn't hang up for me and I can successfully train the model. I was a bit too excited with 2.13 fixing the \"placeholder tensor\" warning from 2.12.\r\n\r\nWould be nice to get some feedback from the team if reproducible.", "`Device /job:localhost/replica:0/task:0/device:GPU:1 is joining a group with size 2, but that group has size 3 (group_key=1)` means that when you are running the function with 2 GPUs, the collective op from previous function call with 3 GPUs might still be pending.\r\n\r\nGenerally it's not a good idea to create multiple `tf.dist.Strategy` in sequence in a production job, as they will share the same collective key and is very likely to cause arbitrary collapse between multiple all-reduces. For this case, try to reset context at the beginning of each test case. Example: https://github.com/tensorflow/tensorflow/blob/2a7efd891d3b16ef82b462d76fd9e61d111bf901/tensorflow/python/distribute/mirrored_strategy_test.py#L355", "I am facing similar issue described by @xinyu-dev. Ubuntu 22.04 and Tensorflow 2.13.0, but running from docker image using `gcr.io/deeplearning-platform-release/tf2-gpu.2-13.py310:m111` as a base, on Vertex AI, with 4 x T4 GPUs. I trained with mirror strategy, which defaults to NCCLAllReduce. The training hangs with 100% GPU and memory utilization. I turned on `NCCL_DEBUG=INFO`, and here is what I have in my logs:\r\n\r\n```\r\nINFO 2023-09-13T15:46:26.702940522Z [resource.labels.taskName: workerpool0-0] NCCL INFO Bootstrap : Using eth0:10.128.0.57<0>\r\nINFO 2023-09-13T15:46:26.702990425Z [resource.labels.taskName: workerpool0-0] NCCL INFO NET/Plugin: Failed to find ncclNetPlugin_v6 symbol.\r\nINFO 2023-09-13T15:46:26.703005755Z [resource.labels.taskName: workerpool0-0] NCCL INFO NET/Plugin: Loaded net plugin FastSocket (v4)\r\nINFO 2023-09-13T15:46:26.703019842Z [resource.labels.taskName: workerpool0-0] NCCL INFO NET/Plugin: Failed to find ncclCollNetPlugin_v6 symbol.\r\nINFO 2023-09-13T15:46:26.703028102Z [resource.labels.taskName: workerpool0-0] NCCL INFO NET/Plugin: Failed to find ncclCollNetPlugin symbol (v4 or v5).\r\nINFO 2023-09-13T15:46:26.703034293Z [resource.labels.taskName: workerpool0-0] NCCL INFO cudaDriverVersion 11040\r\nINFO 2023-09-13T15:46:26.703040462Z [resource.labels.taskName: workerpool0-0] NCCL version 2.13.4+cudaCUDA_MAJOR.CUDA_MINOR\r\nINFO 2023-09-13T15:46:26.893955808Z [resource.labels.taskName: workerpool0-0] NCCL INFO NET/FastSocket : Tx CPU start: -2\r\nINFO 2023-09-13T15:46:26.894000335Z [resource.labels.taskName: workerpool0-0] NCCL INFO NET/FastSocket : Rx CPU start: -2\r\nINFO 2023-09-13T15:46:26.894009348Z [resource.labels.taskName: workerpool0-0] NCCL INFO NET/FastSocket : Flow placement enabled.\r\nINFO 2023-09-13T15:46:26.894015649Z [resource.labels.taskName: workerpool0-0] NCCL INFO NET/FastSocket : queue skip: 0\r\nINFO 2023-09-13T15:46:26.894021171Z [resource.labels.taskName: workerpool0-0] NCCL INFO NET/FastSocket : Using [0]eth0:10.128.0.57<0> [1]veth46fef7a:fe80::ec4e:4ff:fe68:e6aa%veth46fef7a<0>\r\nINFO 2023-09-13T15:46:26.894045883Z [resource.labels.taskName: workerpool0-0] NCCL INFO NET/FastSocket plugin initialized\r\nINFO 2023-09-13T15:46:26.894052051Z [resource.labels.taskName: workerpool0-0] NCCL INFO Using network FastSocket\r\nINFO 2023-09-13T15:46:26.894058304Z [resource.labels.taskName: workerpool0-0] NCCL INFO Using network FastSocket\r\nINFO 2023-09-13T15:46:26.894064036Z [resource.labels.taskName: workerpool0-0] NCCL INFO Using network FastSocket\r\nINFO 2023-09-13T15:46:26.894069715Z [resource.labels.taskName: workerpool0-0] NCCL INFO Using network FastSocket\r\nINFO 2023-09-13T15:46:26.894075386Z [resource.labels.taskName: workerpool0-0] NCCL INFO PXN Disabled as plugin is v4\r\nINFO 2023-09-13T15:46:26.894079843Z [resource.labels.taskName: workerpool0-0] NCCL INFO Trees [0] 2/-1/-1->1->0 [1] 2/-1/-1->1->0\r\nINFO 2023-09-13T15:46:26.894084842Z [resource.labels.taskName: workerpool0-0] NCCL INFO P2P Chunksize set to 131072\r\nINFO 2023-09-13T15:46:26.894089473Z [resource.labels.taskName: workerpool0-0] NCCL INFO Channel 00/02 : 0 1 2 3\r\nINFO 2023-09-13T15:46:26.894094426Z [resource.labels.taskName: workerpool0-0] NCCL INFO Channel 01/02 : 0 1 2 3\r\nINFO 2023-09-13T15:46:26.894101796Z [resource.labels.taskName: workerpool0-0] NCCL INFO Trees [0] 3/-1/-1->2->1 [1] 3/-1/-1->2->1\r\nINFO 2023-09-13T15:46:26.894108081Z [resource.labels.taskName: workerpool0-0] NCCL INFO P2P Chunksize set to 131072\r\nINFO 2023-09-13T15:46:26.894112965Z [resource.labels.taskName: workerpool0-0] NCCL INFO Trees [0] 1/-1/-1->0->-1 [1] 1/-1/-1->0->-1\r\nINFO 2023-09-13T15:46:26.894118387Z [resource.labels.taskName: workerpool0-0] NCCL INFO P2P Chunksize set to 131072\r\nINFO 2023-09-13T15:46:26.894124008Z [resource.labels.taskName: workerpool0-0] NCCL INFO Trees [0] -1/-1/-1->3->2 [1] -1/-1/-1->3->2\r\nINFO 2023-09-13T15:46:26.894129976Z [resource.labels.taskName: workerpool0-0] NCCL INFO P2P Chunksize set to 131072\r\n```", "> Same issue here! Using 4GPU for distributed training on Ubuntu 22.04 with Tensorflow 2.13 hangs at the “compiled cluster using the XLA” line. Issue solved by downgrading to 2.12\r\n\r\nYou may refer to my issue in #62234 .\r\n\r\nCurrently, using RING instead of NCCL is a temporary workaround (https://github.com/edwardyehuang/iSeg/blob/master/utils/distribution_utils.py).\r\n\r\nAnother workaround (2.13 only atm), use `conda install -c conda-forge tensorflow-gpu`, instead docker or pip.\r\n\r\nBesides, if anyone has a tf-nightly GPU wheel on Mar 17 and April 27, please share it with me so I can test it and see if the pull request https://github.com/tensorflow/tensorflow/pull/60001 or https://github.com/tensorflow/tensorflow/pull/59424 cause this issue.\r\n", "> > Same issue here! Using 4GPU for distributed training on Ubuntu 22.04 with Tensorflow 2.13 hangs at the “compiled cluster using the XLA” line. Issue solved by downgrading to 2.12\r\n> \r\n> You may refer to my issue in #62234 .\r\n> \r\n> Currently, using RING instead of NCCL is a temporary workaround (https://github.com/edwardyehuang/iSeg/blob/master/utils/distribution_utils.py).\r\n> \r\n> Another workaround (2.13 only atm), use `conda install -c conda-forge tensorflow-gpu`, instead docker or pip.\r\n> \r\n> Besides, if anyone has a tf-nightly GPU wheel on Mar 17 and April 27, please share it with me so I can test it and see if the pull request #60001 or #59424 cause this issue.\r\n\r\nAnother thing worth to attention: Why the third-party (conda-forge) conda build can avoid this issue? (the TensorFlow in the docker image is directly installed from pip)", "same issue here. anyone finds solutions?", "Upgrade the NVIDIA driver >= 545 and the issue should be addressed", "I got a similar error on the NVIDIA GPU while using tensorflow-federated and nest_asyncio packages. The error appeared when I updated tensorflow-federated package version from 0.38.0 to 0.73.0. I tried updating nest_asyncio, but it didn't help. So I just muted that message. \r\n\r\n`import os`\r\n`os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"1\"`\r\n\r\n[Check](https://stackoverflow.com/questions/76912213/tf2-13-local-rendezvous-recv-item-cancelled) for details.", "Similar error. Fixed it by removing the steps_per_epoch argument from model.fit() and model.evaluate()\r\n\r\nimport sys\r\nfrom matplotlib import pyplot\r\nfrom keras.utils import to_categorical\r\nfrom keras.models import Sequential\r\nfrom keras.layers import Conv2D\r\nfrom keras.layers import MaxPooling2D\r\nfrom keras.layers import Dense\r\nfrom keras.layers import Flatten\r\nfrom keras.optimizers import SGD\r\nfrom tensorflow.keras.preprocessing.image import ImageDataGenerator\r\nimport tensorflow as tf\r\nimport numpy as np\r\n\r\nphysical_devices = tf.config.list_physical_devices('GPU')\r\ntry:\r\n tf.config.experimental.set_memory_growth(physical_devices[0], True)\r\nexcept:\r\n # Invalid device or cannot modify virtual devices once initialized.\r\n pass\r\n\r\n # define cnn model\r\ndef define_model():\r\n model = Sequential()\r\n model.add(Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same', input_shape=(200, 200, 3)))\r\n model.add(MaxPooling2D((2, 2)))\r\n model.add(Flatten())\r\n model.add(Dense(128, activation='relu', kernel_initializer='he_uniform'))\r\n model.add(Dense(1, activation='sigmoid'))\r\n # compile model\r\n opt = SGD(learning_rate=0.001, momentum=0.9)\r\n model.compile(optimizer=opt, loss='binary_crossentropy', metrics=['accuracy'])\r\n return model\r\n\r\n# create data generator\r\ndatagen = ImageDataGenerator(rescale=1.0/255.0)\r\nmodel = define_model()\r\n\r\n# prepare iterators\r\ntrain_it = datagen.flow_from_directory('/workspace/workspace/cats_and_dogs_data/dogs-vs-cats/train/',\r\n class_mode='binary', batch_size=64, target_size=(200, 200))\r\ntest_it = datagen.flow_from_directory('/workspace/workspace/cats_and_dogs_data/dogs-vs-cats/test1/',\r\n class_mode='binary', batch_size=64, target_size=(200, 200))\r\n\r\n# fit model\r\nhistory = model.fit(train_it, validation_data=test_it, epochs=20, verbose=1)\r\n\r\n\r\n# evaluate model\r\n_, acc = model.evaluate(test_it, verbose=1)\r\nprint('> %.3f' % (acc * 100.0))\r\n\r\n\r\n" ]